Journal articles on the topic 'Analysis crowd'
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Husman, Muhammad Afif, Waleed Albattah, Zulkifli Zainal Abidin, Yasir Mohd Mustafah, Kushsairy Kadir, Shabana Habib, Muhammad Islam, and Sheroz Khan. "Unmanned Aerial Vehicles for Crowd Monitoring and Analysis." Electronics 10, no. 23 (November 29, 2021): 2974. http://dx.doi.org/10.3390/electronics10232974.
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Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for crowd analysis reasons, relieving the relevant authorities of the venues’ inadequacies and of concerns of inaccessible locations and situation. This paper reviews existing literature sources regarding the use of aerial vehicles for crowd monitoring and analysis purposes. Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed. In addition, ethical and privacy issues surrounding the use of this technology are presented.
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Elbishlawi, Sherif, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, and Mostafa M. Mohamed. "Deep Learning-Based Crowd Scene Analysis Survey." Journal of Imaging 6, no. 9 (September 11, 2020): 95. http://dx.doi.org/10.3390/jimaging6090095.
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Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.
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Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Wan Noorshahida Mohd Isa, Jia Uddin, and Norra Abdullah. "Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage." Sensors 22, no. 14 (July 7, 2022): 5102. http://dx.doi.org/10.3390/s22145102.
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Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.
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Yugendar, Poojari, and K. V. R. Ravishankar. "Crowd Behavioural Analysis at a Mass Gathering Event." Journal of KONBiN 46, no. 1 (June 1, 2018): 5–20. http://dx.doi.org/10.2478/jok-2018-0020.
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Abstract Religious occasions, gathering at fairs and terminals, are the events of crowd gatherings. Such gatherings act as severe threats for crowds because of high density in less space, which ends up in adverse outcomes resulting in crowd stampedes. The movement of an individual person in a crowd is influenced by the physical factors. In the present study, characteristics like age, gender, group size, child holding, child carrying, people with luggage and without luggage are considered for crowd behaviour analysis. The average speed of the crowd movement was observed as 0.86 m/s. The statistical analysis concluded that there was a significant effect of age, gender, density and luggage on the crowd walking speed. Multi-linear regression (MLR) model was developed between crowd speed and significant factors observed from the statistical analysis. Location 1 data was used for the model development. This developed model was validated using Location 2 data. Gender has more significant effect on speed followed by luggage and age. This study helps in proper dispersal of crowd in a planned manner to that of diversified directional flow that exist during crowd gathering events.
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R, Shaamili. "A Research Perceptive on Deep Learning Framework for Pedestrian Detection in a Crowd." Computational Intelligence and Machine Learning 3, no. 2 (October 14, 2022): 9–14. http://dx.doi.org/10.36647/ciml/03.02.a002.
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In populated cities, we often find crowded events like political meetings, religious festivals, music concerts, and events in shopping malls, which have more safety issues. Smart surveillance systems are used in big cities to keep crowds safe and make crowd security less complicated and more accurate. However, the surveillance systems proposed for a crowd are monitored by human agents, which are inefficient, error-prone, and overwhelming. Even with deep learning-based feature engineering in crowds, many variants of crowd analysis still lack attention and are technically unaddressed. Considering this scenario, the smart system requires the most advanced techniques to monitor the security of the crowd. Crowd analysis is commonly divided into crowd statics and behavior analysis. This paper explores more about crowd behaviour analysis, pedestrian and group detection which describes the movements that are noticed in the crowd image. Subsequently, the issues of the current methodology of pedestrian detection, datasets, and evaluation criteria are analyzed. Keyword : Crowd Analysis, Pedestrian and group detection, deep learning, Crowd IoT analysis, Human Activity Recognition.
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JOHANSSON, ANDERS, DIRK HELBING, HABIB Z. AL-ABIDEEN, and SALIM AL-BOSTA. "FROM CROWD DYNAMICS TO CROWD SAFETY: A VIDEO-BASED ANALYSIS." Advances in Complex Systems 11, no. 04 (August 2008): 497–527. http://dx.doi.org/10.1142/s0219525908001854.
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The study of crowd dynamics is interesting because of the various self-organization phenomena resulting from the interactions of many pedestrians, which may improve or obstruct their flow. Besides formation of lanes of uniform walking direction and oscillations at bottlenecks at moderate densities, it was recently discovered that stop-and-go waves [D. Helbing et al., Phys. Rev. Lett.97 (2006) 168001] and a phenomenon called "crowd turbulence" can occur at high pedestrian densities [D. Helbing et al., Phys. Rev. E75 (2007) 046109]. Although the behavior of pedestrian crowds under extreme conditions is decisive for the safety of crowds during the access to or egress from mass events as well as for situations of emergency evacuation, there is still a lack of empirical studies of extreme crowding. Therefore, this paper discusses how one may study high-density conditions based on suitable video data. This is illustrated at the example of pilgrim flows entering the previous Jamarat Bridge in Mina, 5 kilometers from the Holy Mosque in Makkah, Saudi-Arabia. Our results reveal previously unexpected pattern formation phenomena and show that the average individual speed does not go to zero even at local densities of 10 persons per square meter. Since the maximum density and flow are different from measurements in other countries, this has implications for the capacity assessment and dimensioning of facilities for mass events. When conditions become congested, the flow drops significantly, which can cause stop-and-go waves and a further increase of the density until critical crowd conditions are reached. Then, "crowd turbulence" sets in, which may trigger crowd disasters. For this reason, it is important to operate pedestrian facilities sufficiently below their maximum capacity and to take measures to improve crowd safety, some of which are discussed in the end.
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Andriyanto, Sidhiq, M. Suyanto, and Sukoco Sukoco. "Implementasi Metode Reynolds menggunakan Simulasi Kerumunan Bebek." INTENSIF 1, no. 2 (August 21, 2017): 75. http://dx.doi.org/10.29407/intensif.v1i2.788.
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"Simulation of Duck Crows Using Reynolds Method" is a study with the aim to find out the behavior of duck breeding crowd. The next goal is to make a crowd simulation using the Reynolds method. Limitations of this research variable is the object of research on adult duck Turi, the method used is Reynolds method. The simulations are made using Unity3D software in the form of 3D and the animation provided is just a running gesture. The method of analysis of this research is using research and development method. The result of the research is the data of duck walking in the crowd to be applied in 3D animation. The end result of the study is a simulation of duck crowds that run on flat fields. Destination directions are affected by mouse input and can avoid obstacles when walking. This simulation uses Reynolds basic rules of cohesion, alignment and separation.The conclusion of the research is that there is a similarity between the simulation of the crowd with the movement of the original duck crowd and the Reynolds method can be applied in the simulation of the duck crowd in 3D. Research produces 3D animation of duck crowds that are given the ability to avoid obstacles and target goals determined by mouse input.
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Denis, Stijn, Ben Bellekens, Abdil Kaya, Rafael Berkvens, and Maarten Weyn. "Large-Scale Crowd Analysis through the Use of Passive Radio Sensing Networks." Sensors 20, no. 9 (May 4, 2020): 2624. http://dx.doi.org/10.3390/s20092624.
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The creation of an automatic crowd estimation system capable of providing reliable, real-time estimates of human crowd sizes would be an invaluable tool for organizers of large-scale events, particularly so in the context of safety management. We describe a set of experiments in which we installed a passive Radio Frequency (RF) sensor network in different environments containing thousands of human individuals and discuss the accuracy with which the resulting measurements can be used to estimate the sizes of these crowds. Depending on the selected training approach, a median crowd estimation error of 184 people could be obtained for a large scale environment which contained 3227 people at its peak. Additionally, we look into the potential benefits of dividing one of our experimental environments into multiple subregions and open up a potentially interesting new topic of research regarding the estimation of crowd flows. Finally, we investigate the combination of our measurements with another sources of crowd-related data: sales data from drink stands within the environment. In doing so, we aim to integrate the concept of an automatic RF-based crowd estimation system into the broader domain of crowd analysis.
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Aiello, Lucia. "Digital Skill Evolution in an Industrial Relationship." International Journal of R&D Innovation Strategy 1, no. 1 (January 2019): 1–15. http://dx.doi.org/10.4018/ijrdis.2019010101.
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Crowdsourcing is a powerful mechanism for doing online work and allows for collaboration. By now, in the media and in business on the Internet, crowdsourcing is recognized as an innovative form of value creation that needs taken seriously. This article provides a framework to propose the relation between crowds and tutorship; it considers the tutor of a crowd as a strategic professional figure in an online community. This is done by the consideration of the different roles, activities and tasks of a tutor through the field analysis of a platform of one company that uses crowdsourcing. This tutor is examined based on the middle-of-the-road theoretical positioned from Porter and Kramer, the value creation, and Suermann and Franzoni, the crows science user contribution patterns. The recently evolution of crowd platforms considers the interaction between companies and crowds based on a “Community of Practice” model of Zucchermaglio and Talamo. Value analysis also considers the differences in roles and tasks in relation to where crowd activity is placed into the value chain of company. In crowdsourcing, “digital people” live in a digital society where every individual has a role and operates in an online community, and those have force points and weakness points. The tutor cans also monitor these points, and push interaction and activities of the crowd. The main theoretical contribution is the looking gap in literature and contributing work to this. Through a qualitative analysis, this article provides evidence of the main activities and the role of digital tutors in an online community. The method utilized is netnography through an online participation and observation of a researcher. In this work, professional figures and new technologies are weighed, and human resource management must consider this as it creates value. This article concludes that a tutor of crowds has a positive behavior, they can stimulate crowds. These positive and active behaviors effect crowd attitudes about the benefits of the community, their expectations, and opinions all of which are fundamental for the growth of online community.
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Negied, Nermin Kamal Abdel-Wahab, Elsayed B. Hemayed, and Magda Fayek. "HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1655025. http://dx.doi.org/10.1142/s0218001416550259.
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This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.
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Borch, Christian. "Body to Body: On the Political Anatomy of Crowds." Sociological Theory 27, no. 3 (September 2009): 271–90. http://dx.doi.org/10.1111/j.1467-9558.2009.01348.x.
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This article challenges the negative image that, since the late 19th century, has been associated with crowds, and it does so by focusing on a number of bodily-anatomic aspects of crowd behavior. I first demonstrate that the work of one of the leading crowd psychologists, Gustave Le Bon, instigated a racist body politics. As a contrast to Le Bon's political program, I examine Walt Whitman's poetry and argue that the crowd may embody a democratic vision that emphasizes the social and political import of sexuality and body-to-body contact. Further, I dispute classical crowd theory's idea of an antagonistic relationship between crowds and individuality. Following Elias Canetti, I claim instead that the bodily compression of crowds in fact liberates individuals and creates a democratic transformation. The analysis results in a rehabilitation of crowds and briefly suggests how a reinterpretation of crowd behavior may inform current debates in social theory.
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Obbo, Aggrey, Pius Ariho, and Evarist Nabaasa. "Towards People Crowd Detection Using Wireless Sensor Networks." European Journal of Technology 6, no. 2 (June 17, 2022): 32–48. http://dx.doi.org/10.47672/ejt.1071.
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Objective: The objective of this study was to examine and propose the use of wireless sensor networks for people crowd detection in resource constrained environments such as developing economies.
Methodology: A systematic review was carried out on current technological trends and application of Wireless Sensor Networks (WSNs) in crowd detection. For this study, focus was on WSN implementation in developing economies, where infrastructure is underdeveloped and people crowds are dynamic and spontaneous. Based on a requirement analysis and knowledge of the inherent challenges of WSNs, a WSN implementation for people crowd detection was proposed.
Findings: Most studies in crowd detection using WSNs, have been in the area of non-people crowds. However issues critical to deployment of WSNs for people crowd detection in developing countries include: the uncontrollable nature of people crowds, under developed physical infrastructure and the inherent challenges of power, computational capacity and broadcast communication characterizing WSNs. Achieving people crowd detection using WSNs therefore, calls for special attention.
Recommendation: To ensure effective people crowd detection, requires taking into consideration connectivity, scalability, performance, security, accuracy and resource utilization of WSNs.
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Chen, Jun, Huan Tan, Katrien Van Nimmen, and Peter Van den Broeck. "Data-Driven Synchronization Analysis of a Bouncing Crowd." Shock and Vibration 2019 (June 11, 2019): 1–23. http://dx.doi.org/10.1155/2019/8528763.
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Vibration serviceability problems concerning lightweight, flexible long-span floors and cantilever structures such as grandstands generally arise from crowd-induced loading, in particular due to bouncing or jumping activities. Predicting the dynamic responses of these structures induced by bouncing and jumping crowds has therefore become a critical aspect of vibration serviceability design. Although accurate models describing the load induced by a single person are available, essential information on the level of synchronization within the crowd is missing. In answer to this lack of information, this paper experimentally investigates the inter- and intraperson variability as well as the global crowd behavior in bouncing crowds. A group size of 48 persons is considered in the experiment whereby the individual body motions are registered synchronously by means of a 3D motion capture system. Preliminary tests verified a new approach to characterize the bouncing motion via markers on the clavicle. Subsequently, the full-scale experimental study considered various crowd spacing parameters, auditory stimuli, and bouncing frequencies. Moreover, special test cases were performed whereby each participant was wearing an eyepatch to exclude visual effects. Through the analysis of 330 test cases, the interperson variability at the bouncing frequency is identified. In addition, the cross-correlation and coherence between participants are analyzed. The coherence coefficients between each pair of participants in the same row or column are calculated and can be described by a lognormal distribution function. The influence of the spatial configurations and visual and auditory stimuli is analyzed. For the considered spatial configurations, no relevant impact on the inter- and intraperson variability in the bouncing motion nor in the global crowd behavior is observed. Visual stimuli are found to enhance the coordination and synchronization. Without eyesight, the participants are feeling uncertain about their bouncing behavior. The results evaluating the auditory cues indicate that significantly higher levels of synchronization and a lower degree of the intraperson variability are attained when a metronome cue is used in comparison to songs where the tempo often varies.
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Nishiyama, Hidefumi. "Crowd surveillance: The (in)securitization of the urban body." Security Dialogue 49, no. 3 (January 4, 2018): 200–216. http://dx.doi.org/10.1177/0967010617741436.
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The recent proliferation of the securitization of crowded places has led to a growth in the development of technologies of crowd behaviour analysis. However, despite the emerging prominence of crowd surveillance in emergency planning, its impacts on our understanding of security and surveillance have received little discussion. Using the case of crowd surveillance in Tokyo, this article examines the ways in which crowds are simulated, monitored and secured through the technology of crowd behaviour analysis, and discusses the implications on the politics of security. It argues that crowd surveillance constitutes a unique form of the biopolitics of security that targets not the individual body or the social body of population, but the urban body of crowd. The power of normalization in crowd surveillance operates in a preemptive manner through the codification of crowd behaviour that is spatially and temporarily specific. The article also interrogates the introduction of crowd surveillance in relation to racialized logics of suspicion and argues that, despite its appearance as non-discriminatory and ‘a-racial’, crowd surveillance entails the racial coding of crowd behaviour and urban space. The article concludes with the introduction of crowd surveillance as a border control technology, which reorients existing modalities of (in)securitization at airports.
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Wang, Qi, Bo Liu, and Jianzhe Lin. "Crowd understanding and analysis." IET Image Processing 15, no. 14 (November 21, 2021): 3411–13. http://dx.doi.org/10.1049/ipr2.12379.
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Montejo-Ráez, A., M. C. Díaz-Galiano, F. Martínez-Santiago, and L. A. Ureña-López. "Crowd explicit sentiment analysis." Knowledge-Based Systems 69 (October 2014): 134–39. http://dx.doi.org/10.1016/j.knosys.2014.05.007.
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Zhan, Beibei, Dorothy N. Monekosso, Paolo Remagnino, Sergio A. Velastin, and Li-Qun Xu. "Crowd analysis: a survey." Machine Vision and Applications 19, no. 5-6 (April 10, 2008): 345–57. http://dx.doi.org/10.1007/s00138-008-0132-4.
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Malhotra, Arvind, and Ann Majchrzak. "Greater associative knowledge variety in crowdsourcing platforms leads to generation of novel solutions by crowds." Journal of Knowledge Management 23, no. 8 (October 14, 2019): 1628–51. http://dx.doi.org/10.1108/jkm-02-2019-0094.
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Purpose The purpose of this study is to offer implications and future research directions related to new organizational forms like crowds. Organizations are increasingly relying on online crowds to innovate through mechanisms such as crowdsourcing, open innovation, innovation challenges and tournaments. To leverage the "wisdom of crowds", crowdsourcing platforms that enable heterogeneous knowledge sharing in crowds lead to novel solution generation by individuals in the crowd. Based on the associative variety memory model of creativity, the authors hypothesize that when a crowd contributes a heterogeneous knowledge in form of a variety of knowledge associations, individual crowd members tend to generate solutions that are more novel. In contrast to the brainstorming view that focuses on ideas as knowledge, the authors propose, test, find and elaborate on implications of crowd sharing of heterogeneous knowledge for the generation of innovation, i.e. novel ideas. The authors coded and analyzed all the posts in 20 innovation challenges leveraging online temporary crowds that were structured to foster knowledge sharing as part of the idea generation process. The analysis shows a positive relationship between the variety of knowledge associations contributed by the crowd and the generation of novel solutions by individuals in the crowd. Further, the variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared. The authors offer four implications and several future directions for research on the new organizational form of online crowds. Design/methodology/approach The authors coded and analyzed all the posts in 20 innovation challenges. They also designed and ran these challenges in collaboration with corporate sponsors. The ideas in the challenge were rated by senior executive at each company using a creative forecasting method. Findings The variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared. Research limitations/implications The authors offer four implications and several future directions for research on the new organizational form of online crowds. Practical implications The authors propose several ways in which companies running innovation challenges can moderate and encourage crowd to generate a variety of knowledge. Originality/value The authors believe that we are the first empirical paper to emphasize and show that associative variety of knowledge sharing in crowds has impact on novel idea generation by crowds. This view is counter to "electronic brainstorming" view where crowd is asked to just generate these ideas and often just submit their ideas to the sponsor. Their view also goes beyond knowledge refinement of ideas by crowds to more of knowledge integration by crowds.
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Sonkar, Riddhi, Sadhana Rathod, Renuka Jadhav, and Deepali Patil. "CROWD ABNORMAL BEHAVIOUR DETECTION USING DEEP LEARNING." ITM Web of Conferences 32 (2020): 03040. http://dx.doi.org/10.1051/itmconf/20203203040.
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Crowd analysis has become an extremely famous research point in the territory of computer vision. Computerized examination of group exercises utilizing reconnaissance recordings is a significant issue for public security since it permits the identification of hazardous groups and where they’re going. We all see how many problems are faced because of the crowd. In our country, many terrorists are there. They plant a bomb in a crowded area which causes a lot of injuries. Thieves are mostly found or always leave in crowded areas so they can easily get an advantage of the crowd. In that situation, crowd analysis is very important. This paper presents the design of the deep learning architecture that provides control over the crowd behavior that will help to avoid violence or any other act which occurs because of the crowd which causes harmful effects to the society. So we are proposing a system that detects abnormal behavior of crowds using deep learning techniques.
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BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah, and Dr Mohd Ali Samsudin. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (January 14, 2022): 1190. http://dx.doi.org/10.12688/f1000research.73156.2.
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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.
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Xu, Han, Xiangxia Ren, Weiguo Song, Jun Zhang, and Rayyan Saidahmed. "Spatial and temporal analysis of the bottleneck flow under different walking states with a moving obstacle." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 1 (January 1, 2023): 013401. http://dx.doi.org/10.1088/1742-5468/aca2a2.
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Abstract The regulation of a moving obstacle on crowd movement offers the possibility to enhance evacuation efficiency in emergency situations. In this paper, a series of controlled experiments are conducted to study the effect of the moving obstacle on crowd dynamics for pedestrians in three different competitive levels, which respectively correspond to three different walking states. The enhancement effects of the moving obstacle on evacuation efficiency for the crowd in the dual-task and high-motivated walking states are confirmed, and the positions of the moving obstacle are crucial. It is found that the moving obstacle diminishes the order of the trajectories for the crowd in the dual-task and normal walking states, while it boosts near the exit for the crowd in the high-motivated walking state. And the moving obstacle makes the linear backward propagations of stop-and-go wave disappear for the crowd in the dual-task and high-motivated walking states, but the frequency of stop behavior increases for the crowd in the dual-task and normal walking states. The profiles of evacuation time show that the moving obstacle impedes the pedestrian flow from the front of the exit and increases evacuation efficiency for the pedestrians near the walls of the exit. The analysis of time headway suggests that the moving obstacle with a gap of 1.0 m or 1.2 m to the exit can reduce the number of the pedestrians waiting near the exit for the crowd in the dual-task and high-motivated walking states. Besides, the gap of 0.8 m between the moving obstacle and the exit makes the conflicts at the exit is increased, but the gap of 1.0 m or 1.2 m makes the number of conflicts at the exit be reduced. This study helps the evacuation management of dense crowds and improves the design of facilities to facilitate pedestrian traffic.
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Shukla, Shivang, Bernard Tiddeman, and Helen C. Miles. "A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator." Remote Sensing 13, no. 14 (July 15, 2021): 2780. http://dx.doi.org/10.3390/rs13142780.
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Crowd size estimation is a challenging problem, especially when the crowd is spread over a significant geographical area. It has applications in monitoring of rallies and demonstrations and in calculating the assistance requirements in humanitarian disasters. Therefore, accomplishing a crowd surveillance system for large crowds constitutes a significant issue. UAV-based techniques are an appealing choice for crowd estimation over a large region, but they present a variety of interesting challenges, such as integrating per-frame estimates through a video without counting individuals twice. Large quantities of annotated training data are required to design, train, and test such a system. In this paper, we have first reviewed several crowd estimation techniques, existing crowd simulators and data sets available for crowd analysis. Later, we have described a simulation system to provide such data, avoiding the need for tedious and error-prone manual annotation. Then, we have evaluated synthetic video from the simulator using various existing single-frame crowd estimation techniques. Our findings show that the simulated data can be used to train and test crowd estimation, thereby providing a suitable platform to develop such techniques. We also propose an automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters. We evaluate the results by applying our new framework to a variety of scenarios with varying crowd sizes. The proposed system gives promising results using widely accepted metrics including MAE, RMSE, Precision, Recall, and F1 score to validate the results.
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Muhammed Anees, V., and G. Santhosh Kumar. "Identification of crowd behaviour patterns using stability analysis." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2829–43. http://dx.doi.org/10.3233/jifs-200667.
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Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.
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Cecaj, Alket, Marco Lippi, Marco Mamei, and Franco Zambonelli. "Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches." IoT 2, no. 1 (January 8, 2021): 33–49. http://dx.doi.org/10.3390/iot2010003.
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The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.
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Cecaj, Alket, Marco Lippi, Marco Mamei, and Franco Zambonelli. "Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches." IoT 2, no. 1 (January 8, 2021): 33–49. http://dx.doi.org/10.3390/iot2010003.
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The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.
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Polyakova, Yu M. "Human Resource Management Based on Modern Crowd Technologies: Crowd Staffing, Crowd Recruiting and Crowd Training." Scientific Research of Faculty of Economics. Electronic Journal 12, no. 3 (September 28, 2020): 16–30. http://dx.doi.org/10.38050/2078-3809-2020-12-3-16-30.
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The rapidly changing conditions of the struggle for talented personnel in the context of mass digitalization require a revision of the methods of personnel management in modern organizations. The aim of the study is to determine the role of crowd-technologies in increasing the efficiency of search, selection and development of personnel in Russian organizations, as well as to develop a system of criteria and indicators for assessing the effectiveness of this type technologies. The scientific works of domestic and foreign scientists, reports of international organizations, the Ministry of Economic Development of the Russian Federation, companies specializing in crowd-technologies were studied. The work used the methods of induction, comparative analysis, benchmarking of the best foreign and Russian practices of using crow- technologies in the field of HR-management and the method of conceptual and methodological modeling. The research conducted by the author made it possible to identify the socio-economic effects of the use of crowd staffing and crowd recruiting in the field of personnel search and selection, crowd training as a method of training employees, and also to propose a number of criteria and indicators for assessing the effectiveness of using these technologies in order to make effective decisions by management personnel.
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Xue, Yiran, Peng Liu, Ye Tao, and Xianglong Tang. "Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model." International Journal of Applied Mathematics and Computer Science 27, no. 1 (March 28, 2017): 181–94. http://dx.doi.org/10.1515/amcs-2017-0013.
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Abstract In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.
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Livshits, Benjamin, and Todd Mytkowicz. "Saving Money While Polling with InterPoll Using Power Analysis." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 159–70. http://dx.doi.org/10.1609/hcomp.v2i1.13168.
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Crowd-sourcing is increasingly being used for providing responses to polls and surveys on a large scale. Companies such as SurveyMonkey and Instant.ly are attempting to make crowd-sourced surveys commonplace, by making it easy to pose survey questions using an easy-to-use UI and retrieve results with a relatively low latency by having dedicated crowds at their disposal. In this paper we argue that the ease with which polls can be created conceals an inherent difficulty: the survey maker does not know how many workers to hire for their survey. Asking too few may lead to samples sizes that `"do not look impressive enough." Asking too many clearly involves spending extra money, which can quickly become costly. Existing crowd-sourcing platforms do not provide help with this, neither, one can argue, do they have any incentive to do so. We present a systematic approach to determining how many samples (i.e. workers) are required to achieve a certain level of statistical significance by showing how to automatically perform power analysis on questions of interest. Using a range of queries we demonstrate that power analysis can save significant amounts of money and time by concluding that frequently, only a handful of results is required to arrive at a certain decision. We have implemented our approach within InterPoll, aprogrammable developer-driven polling system that uses a generic crowd (Mechanical Turk) as a back-end. Power analysis is automatically performed given both the structure of the query and the data that is being polled from the crowd. In all of our studies we are able to obtain statistically significant answers for under $30, with most costing less than $10. Our approach saves both time and money for the survey maker.
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BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, Fahmid Al Farid, Dr Jia Uddin, Norra Abdullah, and Dr Mohd Ali Samsudin. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (November 24, 2021): 1190. http://dx.doi.org/10.12688/f1000research.73156.1.
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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.
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Zhao, Rongyong, Ping Jia, Yan Wang, Cuiling Li, Yunlong Ma, and Zhishu Zhang. "Acceleration-critical density time-delay model for crowd stability analysis based on Lyapunov theory." MATEC Web of Conferences 355 (2022): 03019. http://dx.doi.org/10.1051/matecconf/202235503019.
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Crowd stability analysis is one of research hotspots to alleviate the severe situation of stampede accidents worldwide. Different from the conventional analysis models for crowd stability based on pedestrian density, this study analyses the characteristics of external disturbances and internal obstacle disturbance based on Lyapunov's theory. The critical range of crowd acceleration in crowd evacuation is obtained, a crowd merging acceleration-critical density time delay model is established, and a stability criterion of acceleration vector based on Lyapunov is obtained based on Lyapunov stability analysis. This provides new information for ensuring the stability of crowd movement in public places, assessing the stability of the crowd in the area, and taking reasonable protection and guidance measures prior to instability of a crowd flow.
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Saglietto, Laurence. "Bibliometric analysis of sharing economy logistics and crowd logistics." International Journal of Crowd Science 5, no. 1 (March 22, 2021): 31–54. http://dx.doi.org/10.1108/ijcs-07-2020-0014.
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Purpose This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options? Design/methodology/approach Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field. Findings The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment. Research limitations/implications The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc. Originality/value Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.
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Kefan, Xie, Yu Song, Sishi Liu, and Jia Liu. "Analysis of crowd stampede risk mechanism." Kybernetes 48, no. 1 (January 14, 2019): 124–42. http://dx.doi.org/10.1108/k-11-2017-0415.
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Purpose The purpose of this paper is to analyze the crowd stampede risk mechanism from the perspective of systems thinking. Design/methodology/approach Causal loop diagram is drawn to outline the non-linear interactions among complex factors across the whole system and dissect the contributory factors of crowd stampede accident. To systematically construct the theoretical framework and find fundamental solutions, co-word analysis with Citespace is used to get the critical data. An agent-based simulation using Pathfinder is conducted to develop a spatial model for the Shanghai Stampede Accident that happened in 2014. Findings The causal loop diagram is formed to not only illustrate the symptomatic solutions with a quick fix but also dissect the fundamental solutions through an underlying systemic analysis. The simulation shows that crowd stampede experiences an interactive process of accumulation, trigger, delay, break and diffusion of risk factors within the crowd system. A linkage effect among the multidimensional characters of individuals and the system accelerates the stampede risk deterioration. There exists delay of the result of effect from the deep-level measure. Practical implications A top-down approach is offered to policymakers for crowd stampede risk protocol design and synergic emergency control that may reduce the risk of the stampede. Originality/value In this study, SDFT paradigm is proposed as the critical solution for the crowd stampede accident. In addition, a chain effect of energy and a linkage effect within the crowd system is illustrated for in-depth understanding of crowd stampede risk.
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Alamri, Abdullah. "Cloud of Things in Crowd Engineering: A Tile-Map-Based Method for Intelligent Monitoring of Outdoor Crowd Density." Sensors 22, no. 9 (April 26, 2022): 3328. http://dx.doi.org/10.3390/s22093328.
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Managing citizen and community safety is one of the most essential services that future cities will require. Crowd analysis and monitoring are also a high priority in the current COVID-19 pandemic scenario, especially because large-scale gatherings can significantly increase the risk of infection transmission. However, crowd tracking presents several complex technical challenges, including accurate people counting and privacy preservation. In this study, using a tile-map-based method, a new intelligent method is proposed which is integrated with the cloud of things and data analytics to provide intelligent monitoring of outdoor crowd density. The proposed system can detect and intelligently analyze the pattern of crowd activity to implement contingency plans, reducing accidents, ensuring public safety, and establishing a smart city. The experimental results demonstrate the feasibility of the proposed model in detecting crowd density status in real-time. It can effectively assist with crowd management tasks such as monitoring, guiding, and managing crowds to ensure safety. In addition, the proposed algorithm provides acceptable performance.
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Huang, Shaonian, Dongjun Huang, and Mansoor Ahmed Khuhro. "Crowd Motion Analysis Based on Social Force Graph with Streak Flow Attribute." Journal of Electrical and Computer Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/492051.
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Over the past decades, crowd management has attracted a great deal of attention in the area of video surveillance. Among various tasks of video surveillance analysis, crowd motion analysis is the basis of numerous subsequent applications of surveillance video. In this paper, a novel social force graph with streak flow attribute is proposed to capture the global spatiotemporal changes and the local motion of crowd video. Crowd motion analysis is hereby implemented based on the characteristics of social force graph. First, the streak flow of crowd sequence is extracted to represent the global crowd motion; after that, spatiotemporal analogous patches are obtained based on the crowd visual features. A weighted social force graph is then constructed based on multiple social properties of crowd video. The graph is segmented into particle groups to represent the similar motion patterns of crowd video. A codebook is then constructed by clustering all local particle groups, and consequently crowd abnormal behaviors are detected by using the Latent Dirichlet Allocation model. Extensive experiments on challenging datasets show that the proposed method achieves preferable results in the application of crowd motion segmentation and abnormal behavior detection.
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Li, Zhouxia, Zhiwen Pan, Xiaoni Wang, Wen Ji, and Feng Yang. "Intelligence level analysis for crowd networks based on business entropy." International Journal of Crowd Science 3, no. 3 (September 2, 2019): 249–66. http://dx.doi.org/10.1108/ijcs-05-2019-0014.
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Purpose Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network. Design/methodology/approach Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy. Findings By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher. Originality/value This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.
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Bandini, Stefania, Simone Calderara, and Rita Cucchiara. "Pattern recognition and crowd analysis." Pattern Recognition Letters 44 (July 2014): 1–2. http://dx.doi.org/10.1016/j.patrec.2014.02.004.
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Mladenow, Andreas, Christine Bauer, and Christine Strauss. "“Crowd logistics”: the contribution of social crowds in logistics activities." International Journal of Web Information Systems 12, no. 3 (August 15, 2016): 379–96. http://dx.doi.org/10.1108/ijwis-04-2016-0020.
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Purpose The paper aims to provide the necessary basis for a novel interdisciplinary research field. Various types and implementations of crowdsourcing have emerged in the market; many of them are related to logistics. While we can identify plenty of crowd logistics applications using information technology capabilities and information sharing in practice, theories behind this phenomenon have received only limited attention. This paper accounts for filling this research gap by analyzing the crowd’s contributions in logistics of goods and information. Design/methodology/approach This paper is part of an ongoing research endeavor in the field of location-based crowdsourcing. It represents conceptual work that builds on a literature review enriched with an in-depth analysis of real-world examples in the field of crowd logistics. Using a scoring method, we provide an example how a company may evaluate the alternatives of crowd logistics. The main approach is an analysis of variants of how the social crowd may be integrated in logistics processes. The work is conceptual in its core. Thereby, we use real-world examples of crowdsourcing applications to underpin the evaluated variants of crowd logistics. Findings The paper presents relevant theoretical background on crowd logistics. The authors differentiate between variants of crowd logistics with their flow of materials, goods and information. Thereby they zoom in the type, significance and process flow of the crowd’s contributions. They discuss potential advantages and challenges of logistics with the performing crowd and deeply discuss opportunities and challenges from a business and from an individual’s perspective. Finally, they highlight a route map for future research directions in this novel interdisciplinary research field. Research limitations/implications As this work is conceptual in its core, generalizations may be drawn only with great care. Still, we are in a position to propose a route map for further research in this area in this paper. Also the integration of an analysis of a scale of real-world applications allows us to highlight our research’s practical relevance and implications. Originality/value The main contribution of this paper is an in-depth analysis and consolidation of innovative crowd logistics applications to provide an overview on recent implementations. The authors propose a categorization scheme and contribute with a route map for further research in the field of crowd logistics.
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Ilyas, Naveed, Ahsan Shahzad, and Kiseon Kim. "Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation." Sensors 20, no. 1 (December 19, 2019): 43. http://dx.doi.org/10.3390/s20010043.
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Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).
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Korbut, Andrei M. "Social Order and Practical Wisdom of Walking in a Crowd." Sociological Journal 24, no. 4 (2018): 8–29. http://dx.doi.org/10.19181/socjour.2018.24.4.6095.
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The article suggests returning to the “crowd” as an object of sociological analysis. Crowds have attracted early sociologists because crowds were visual embodiments of social forces that surpass individuals and also served as a symbol of the profound social transformations which were taking place in the late 19th and early 20th centuries. Analyzing crowds allowed for the first sociologists (G. Simmel, R. Park, M. Weber, E. Durkheim) to oppose the psychological interpretation of mass social phenomena with a purely sociological approach. However, in the second half of the 20th century sociologists had lost almost all interest in the crowd, as it did not meet the interests of researchers of “large” social structures, nor the interests of the proponents of interactionist approaches. This article shows that the crowd can again be made interesting for sociology if we were to consider it from the point of view of the everyday practices of the participants. In these everyday practices a specific form of phronesis, i.e. practical wisdom, technical skill coupled with moral judgment about which action is good and which is not, is implemented. It is shown here that the study of the practical wisdom of walking in a crowd requires special concepts and methods that can be found in phenomenology and ethnomethodology. The article suggests using three such concepts for the analysis of crowds: phenomenal field, oriented object, and figuration of details. With the help of these concepts, the methods of the crowd’s situated social order production are analyzed in relation to the management of speed and trajectories of movement, following one another, walkers’ stopping and slowing down, and joining the crowd. This analysis shows that the joint production of the crowd’s social order by its participants is a situated practice, i.e. it consists of making the local scenes of everyday life familiar and accountable, and of assessing the local adequacy of the actions performed.
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Zeitz, Kathryn, Pari Delir Haghighi, Frada Burstein, and Jeffrey Williams. "Understanding the drivers on medical workloads: an analysis of spectators at the Australian Football League." Australian Health Review 37, no. 3 (2013): 402. http://dx.doi.org/10.1071/ah13032.
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Objective. The present study was designed to further understand the psychosocial drivers of crowds impacting on the demand for healthcare. This involved analysing different spectator crowds for medical usage at mass gatherings; more specifically, did different football team spectators (of the Australian Football League) generate different medical usage rates. Methods. In total, 317 games were analysed from 10 venues over 2 years. Data were analysed by the ANOVA and Pearson correlation tests. Results. Spectators who supported different football teams generated statistically significant differences in patient presentation rates (PPR) (F15, 618 = 1.998, P = 0.014). The present study confirmed previous findings that there is a positive correlation between the crowd size and PPR at mass gatherings but found a negative correlation between density and PPR (r = –0.206, n = 317, P < 0.0005). Conclusions. The present study has attempted to scientifically explore psychosocial elements of crowd behaviour as a driver of demand for emergency medical care. In measuring demand for emergency medical services there is a need to develop a more sophisticated understanding of a variety of drivers in addition to traditional metrics such as temperature, crowd size and other physical elements. In this study we saw that spectators who supported different football teams generated statistically significant differences in PPR. What is known about this topic? Understanding the drivers of emergency medical care is most important in the mass gathering setting. There has been minimal analysis of psychological ‘crowd’ variables. What does this paper add? This study explores the psychosocial impact of supporting a different team on the PPR of spectators at Australian Football League matches. The value of collecting and analysing these types of data sets is to support more balanced planning, better decision support and knowledge management, and more effective emergency medical demand management. What are the implications for practitioners? This information further expands the body of evidence being created to understand the drivers of emergency medical demand and usage. In addition, it supports the planning and management of emergency medical and health-related requirements by increasing our understanding of the effect of elements of ‘crowd’ that impact on medical usage and emergency healthcare.
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Sun, Libo, and Norman Badler. "Exploring the Consequences of Crowd Compression Through Physics-Based Simulation." Sensors 18, no. 12 (November 27, 2018): 4149. http://dx.doi.org/10.3390/s18124149.
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Statistical analysis of accidents in recent years shows that crowd crushes have become significant non-combat, non-environmental public disasters. Unlike common accidents such as fires, crowd crushes may occur without obvious external causes, and may arise quickly and unexpectedly in otherwise normal surroundings. We use physics-based simulations to understand the processes and consequences of compressive forces on high density static crowds consisting of up to 400 agents in a restricted space characterized by barriers to free movement. According to empirical observation and experimentation by others, we know that local high packing density is an important factor leading to crowd crushes and consequent injuries. We computationally verify our hypothesis that compressive forces create high local crowd densities which exceed human tolerance. Affected agents may thus be unable to move or escape and will present additional movement obstacles to others. Any high density crowd simulation should therefore take into account these possible negative effects on crowd mobility and behavior. Such physics-based simulations may therefore assist in the design of crowded spaces that could reduce the possibility of crushes and their consequences.
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Sharma, Vipul, Roohie Naaz Mir, and Chandrapal Singh. "Scale-aware CNN for crowd density estimation and crowd behavior analysis." Computers and Electrical Engineering 106 (March 2023): 108569. http://dx.doi.org/10.1016/j.compeleceng.2022.108569.
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Zeng, Dongjun, Haoqi Wang, and Jun Chen. "Dynamic Reliability Analysis of Large-Span Structures under Crowd Bouncing Excitation." Buildings 12, no. 3 (March 10, 2022): 332. http://dx.doi.org/10.3390/buildings12030332.
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Bouncing is one of the most common human crowd activities on civil infrastructures such as sports stadiums and concert halls, where the audience tends to make their bodies jump up and down to celebrate or participate in sport and musical events. Dynamic loads are thus generated and exerted on the structures, giving unpleasant structural vibration, which may affect the functionality of the structure or even lead to a panic of the crowd. Although researchers have studied human-induced vibration from many perspectives including load models, calculation methods, criteria for serviceability evaluation, etc., there has been minimal work regarding crowd-induced reliability analysis, mainly because the stochastic feature of the crowd load as well as the mechanism describing the crowd–structure interaction is still not clear. In this paper, a framework to calculate crowd-induced structural vibration that considers the crowd–structure interaction effect is proposed and is validated through an experimental test. The dynamic parameters of the bouncing person in the crowd are adopted from a previous statistical study. The feasibility of a probability density evolution method (PDEM) is proved to be effective to calculate structural stochastic vibration under the bouncing crowd. The dynamic reliability of the structure is thus analyzed based on the stochastic responses. Results show that the consideration of the crowd–structure interaction effect significantly affects the dynamic reliability, which is also dependent on various factors including bouncing frequency, failure criteria, limit threshold, human model parameter distribution, etc. This paper provides a foundation for the performance-based vibration serviceability design of large-span structures.
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Ebrahimpour, Wan, Cervantes, Luo, and Ullah. "Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis." ISPRS International Journal of Geo-Information 8, no. 10 (October 7, 2019): 440. http://dx.doi.org/10.3390/ijgi8100440.
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Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.
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Cutler, Mitchell C., Mylan R. Cook, Mark K. Transtrum, and Kent L. Gee. "Spectral-based cluster analysis of noise from collegiate sporting events." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A49. http://dx.doi.org/10.1121/10.0015500.
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This paper presents on studies to characterize crowd response from recordings taken from 30 collegiate sporting events. Inferring crowd response from raw acoustic signals is challenging because they contain complex combinations of acoustic sources including crowd noise, music, individual voices, and PA system. First, the distributions of recorded half-second interval overall sound pressure levels from basketball and volleyball games, both men’s and women’s, are analyzed with regard to crowd size and venue. Using 24 one-third octave bands between 50 Hz and 10 kHz, half-second spectral levels from each type of game are then analyzed. Based on principal component analysis, 87% of the spectral variation in the signals can be represented with three principal components, regardless of sport, venue, or crowd composition. Using the resulting three-dimensional component coefficient representation, clustering analysis (using Gaussian mixture models) then finds nine different clusters. These clusters separate audibly distinct signals and represent various combinations of acoustic sources, including crowd noise, music, individual voices, and PA system.
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Zhu, Wenjie, Rongyong Zhao, Hao Zhang, Ping Jia, Yan Wang, Cuiling Li, and Yunlong Ma. "Crowd Stability Analysis Based on Pedestrian Abnormal Postures." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012062. http://dx.doi.org/10.1088/1742-6596/2224/1/012062.
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Abstract Abnormal behaviors of pedestrians in crowd gathering public places are important factors affecting the stability of crowd flow. Pedestrian abnormal postures are important manifestation of abnormal behaviors, which often leads to local turbulence, disturbance and density-speed fluctuations. It is urgent to discover the disturbance mechanism of abnormal pedestrian posture on the stability of crowd flow. This study intends to establish machine vision, kinematics, dynamic models and crowd confluence dynamic models for typical abnormal pedestrian postures in public places. We mainly use computer vision related technology based to recognize abnormal postures of pedestrians in videos, constructs a network matrix of key posture nodes, and studys the kinematics characteristics of abnormal posture nodes. Considering the number of pedestrians and the characteristics of the architectural scenes, we design a workflow to select the appropriate macro or micro dynamic model to build the crowd flow model. To validate the propuesd model, case in Shanghai Hongqiao railway station is studied.
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Liu, Haiyan, Jing Li, Qiang Guo, Youwei Zhang, Chuanwei Lu, Fang Hu, and Hongjian Wu. "Extraction and Analysis of Crowd Activity Vergence Model in Space-Time Vector Field." Journal of Physics: Conference Series 2294, no. 1 (June 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2294/1/012031.
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Abstract The vergence model of crowd activity is one of the core contents of human mobility research. Traditional methods do not consider the mobility of crowd activities in terms of extracting vergence models. In this paper, the model extraction problem is transformed into a time series clustering problem, and the mobility of crowd activities is dynamically modeled by introducing vector field theory. Then, the vergence of the crowd is calculated by the divergence. Finally, a time series composed of the crowd vergence is constructed to obtain the main vergence model of crowd activity through clustering. The method proposed in this paper is experimentally verified on the Didi Chuxing data in Haikou City, and four main vergence models of the crowd activity are extracted, which proves that the method proposed in this paper is effective and provides research ideas and method support for exploring human mobility.
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Lu, Jiaqi, Shijun Liu, Lizhen Cui, Li Pan, and Lei Wu. "Crowd wisdom drives intelligent manufacturing." International Journal of Crowd Science 1, no. 1 (March 6, 2017): 39–47. http://dx.doi.org/10.1108/ijcs-01-2017-0002.
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Purpose A fundamental problem for intelligent manufacturing is to equip the agents with the ability to automatically make judgments and decisions. This paper aims to introduce the basic principle for intelligent crowds in an attempt to show that crowd wisdom could help in making accurate judgments and proper decisions. This further shows the positive effects that crowd wisdom could bring to the entire manufacturing process. Design/methodology/approach Efforts to support the critical role of crowd wisdom in intelligent manufacturing involve theoretical explanation, including a discussion of several prevailing concepts, such as consumer-to-business (C2B), crowdfunding and an interpretation of the contemporary Big Data mania. In addition, an empirical study with three business cases was conducted to prove the conclusion that our ideas could well explain the current business phenomena and guide the future of manufacturing. Findings This paper shows that crowd wisdom could help make accurate judgments and proper decisions. It further shows the positive effects that crowd wisdom could bring to the entire manufacturing process. Originality/value The paper highlights the importance of crowd wisdom in manufacturing with sufficient theoretical and empirical analysis, potentially providing a guideline for future industry.
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Wax, Amy, Andrea Hopmeyer, Paschal N. Dulay, and Tal Medovoy. "Commuter College Student Adjustment: Peer Crowd Affiliation as a Driver of Loneliness, Belongingness, and Risk Behaviors." Emerging Adulthood 7, no. 5 (June 11, 2018): 363–69. http://dx.doi.org/10.1177/2167696818781128.
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Although previous research has clearly demonstrated the impact that peer crowd affiliation has on socioemotional and risk-related outcomes, very few studies have investigated this relation in samples of emerging adults, and even fewer have focused specifically on commuter college students. Accordingly, the current study aimed to fill this gap in the literature by exploring the relationship between peer crowds and college adjustment at a commuter school. Participants were 663 students at a large public university in Southern California (campus population of 92% commuters). Factor analytic results indicated the presence of four crowd dimensions on campus: (a) social/partiers, (b) creatives and activists, (c) campus active, and (d) international students. Furthermore, path analysis results indicated that these crowd dimensions predict loneliness, college belongingness, and risk behaviors. Overall, the results of this study indicate the presence of a peer crowd landscape unique to commuter schools that has important implications for student adjustment.
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Bielsa, Esperança. "From ‘the people’ to the crowd: The push for independence in Catalonia." Social Science Information 60, no. 3 (June 17, 2021): 395–412. http://dx.doi.org/10.1177/05390184211021354.
Full textAbstract:
This article examines the sociological value of Elias Canetti’s work on crowds and power. It explores crowd action and imagery in the push for Catalan independence through the analysis of materials published on Twitter by Tsunami Democràtic, which emerged to coordinate the response to the sentencing of Catalan political leaders after the unilateral declaration of independence. It then goes on to discuss how a crowd-based approach offers a supplementary perspective to contemporary studies of populism, on the one hand, and to accounts that primarily focus on the role of social media in organizing political protest movements, on the other. An analysis of crowds not only avoids both methodological holism and methodological individualism. It also helps to understand why so many people were mobilized beyond the power of concepts, ideologies and discourse.