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Добірка наукової літератури з теми "Crowd dataset"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Crowd dataset"

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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Анотація:

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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Bhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Mohammad Ahsanul Haque, Jia Uddin, Wan Noorshahida Mohd Isa, Mohd Nizam Husen, and Norra Abdullah. "A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network." PeerJ Computer Science 8 (March 25, 2022): e895. http://dx.doi.org/10.7717/peerj-cs.895.

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Анотація:

This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.

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Alafif, Tarik, Anas Hadi, Manal Allahyani, Bander Alzahrani, Areej Alhothali, Reem Alotaibi, and Ahmed Barnawi. "Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds." Electronics 12, no. 5 (February 28, 2023): 1165. http://dx.doi.org/10.3390/electronics12051165.

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Анотація:

Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors in small and large-scale crowd videos. In small-scale crowd videos, a ResNet-50 pre-trained CNN model is fine-tuned to verify whether every frame is normal or abnormal in the spatial domain. If anomalous behaviors are observed, a motion-based individual detection method based on the magnitudes and orientations of Horn–Schunck optical flow is proposed to locate and track individuals with abnormal behaviors. A Kalman filter is employed in large-scale crowd videos to predict and track the detected individuals in the subsequent frames. Then, means and variances as statistical features are computed and fed to the RF classifier to classify individuals with abnormal behaviors in the temporal domain. In large-scale crowds, we fine-tune the ResNet-50 model using a YOLOv2 object detection technique to detect individuals with abnormal behaviors in the spatial domain. The proposed method achieves 99.76% and 93.71% of average area under the curves (AUCs) on two public benchmark small-scale crowd datasets, UMN and UCSD, respectively, while the large-scale crowd method achieves 76.08% average AUC using the HAJJv2 dataset. Our method outperforms state-of-the-art methods using the small-scale crowd datasets with a margin of 1.66%, 6.06%, and 2.85% on UMN, UCSD Ped1, and UCSD Ped2, respectively. It also produces an acceptable result in large-scale crowds.

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Ren, Guoyin, Xiaoqi Lu, and Yuhao Li. "Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis." Journal of Sensors 2022 (August 12, 2022): 1–19. http://dx.doi.org/10.1155/2022/7247757.

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Анотація:

Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.

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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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Анотація:

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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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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Анотація:

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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Wu, Junfeng, Zhiyang Li, Wenyu Qu, and Yizhi Zhou. "One Shot Crowd Counting with Deep Scale Adaptive Neural Network." Electronics 8, no. 6 (June 21, 2019): 701. http://dx.doi.org/10.3390/electronics8060701.

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Анотація:

This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd in the image. The proposed DSA-CNN consists of two parts: the seven layers CNN network structure and DSA modules. In order to ensure the proposed method is robust to camera perspective effect, DSA-CNN has adopted different sizes of filters in the network and combines them ingeniously. In order to reduce the depth of the data to increase the speed of training, the proposed method utilized 1 × 1 filter in DSA module. To validate the effectiveness of the proposed model, we conducted comparative experiments on four popular public datasets (ShanghiTech dataset, UCF_CC_50 dataset, WorldExpo’10 dataset and UCSD dataset). We compare the proposed method with other well-known algorithms on the MAE and MSE indicators, such as MCNN, Switching-CNN, CSRNet, CP-CNN and Cascaded-MTL. Experimental results show that the proposed method has excellent performance. In addition, we found that the proposed model is easily trained, which further increases the usability of the proposed model.

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Kaya, Abdil, Stijn Denis, Ben Bellekens, Maarten Weyn, and Rafael Berkvens. "Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation." Data 5, no. 2 (June 9, 2020): 52. http://dx.doi.org/10.3390/data5020052.

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Анотація:

Organisers of events attracting many people have the important task to ensure the safety of the crowd on their venue premises. Measuring the size of the crowd is a critical first step, but often challenging because of occlusions, noise and the dynamics of the crowd. We have been working on a passive Radio Frequency (RF) sensing technique for crowd size estimation, and we now present three datasets of measurements collected at the Tomorrowland music festival in environments containing thousands of people. All datasets have reference data, either based on payment transactions or an access control system, and we provide an example analysis script. We hope that future analyses can lead to an added value for crowd safety experts.

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Shao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang, and Huayi Zhan. "A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV." Sensors 20, no. 19 (September 28, 2020): 5550. http://dx.doi.org/10.3390/s20195550.

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Анотація:

Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.

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Zhang, Cong, Kai Kang, Hongsheng Li, Xiaogang Wang, Rong Xie, and Xiaokang Yang. "Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset." IEEE Transactions on Multimedia 18, no. 6 (June 2016): 1048–61. http://dx.doi.org/10.1109/tmm.2016.2542585.

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Більше джерел

Дисертації з теми "Crowd dataset"

Choudhury, Ananya. "WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72256.

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Human behavior is a key factor influencing the spread of infectious diseases. Individuals adapt their daily routine and typical behavior during the course of an epidemic -- the adaptation is based on their perception of risk of contracting the disease and its impact. As a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data collection methods are not well suited to support the task of acquiring human behavior related information; especially as it pertains to epidemic planning and response. Internet-based methods are an attractive complementary mechanism for collecting behavioral information. Systems such as Amazon Mechanical Turk (MTurk) and online survey tools provide simple ways to collect such information. This thesis explores new methods for information acquisition, especially behavioral information that leverage this recent technology. Here, we present the design and implementation of a crowd-sourced surveillance data acquisition system -- WiSDM. WiSDM is a web-based application and can be used by anyone with access to the Internet and a browser. Furthermore, it is designed to leverage online survey tools and MTurk; WiSDM can be embedded within MTurk in an iFrame. WiSDM has a number of novel features, including, (i) ability to support a model-based abductive reasoning loop: a flexible and adaptive information acquisition scheme driven by causal models of epidemic processes, (ii) question routing: an important feature to increase data acquisition efficacy and reduce survey fatigue and (iii) integrated surveys: interactive surveys to provide additional information on survey topic and improve user motivation. We evaluate the framework's performance using Apache JMeter and present our results. We also discuss three other extensions of WiSDM: Adapter, Synthetic Data Generator, and WiSDM Analytics. The API Adapter is an ETL extension of WiSDM which enables extracting data from disparate data sources and loading to WiSDM database. The Synthetic Data Generator allows epidemiologists to build synthetic survey data using NDSSL's Synthetic Population as agents. WiSDM Analytics empowers users to perform analysis on the data by writing simple python code using Versa APIs. We also propose a data model that is conducive to survey data analysis.
Master of Science

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CONIGLIARO, Davide. "Spectator crowd: a social signal processing perspective." Doctoral thesis, 2016. http://hdl.handle.net/11562/940037.

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Questa tesi propone un nuovo tipo di analisi in Computer Vision basato sulla folla di spettatori, ovvero una folla formata da persone riunite per guardare qualcosa di specifico che attira il loro interesse. Alcuni scenari tipici in cui è presente la folla di spettatori sono gli stadi, i teatri, le aule, ecc., questi scenari condividono alcuni aspetti con la folla tradizionale; per esempio, il fatto che osservo molte persone contemporaneamente e quindi l'analisi sul singolo individuo è complicata; tuttavia, nel nostro caso, la dinamica delle persone è vincolata dalla struttura architettonica; in particolare, le persone tendono a rimanere in una posizione fissa per la maggior parte del tempo, limitando la loro attività ad applaudire, alzare le mani, supportare i giocatori o discutere con i vicini. Per affrontare questa problematica, abbiamo deciso di seguire un approccio di Social Signal Processing basato su tecniche di Computer Vision e teorie sociologiche. In particolare, mostriamo risultati concreti su come sia possibile distinguere il comportamento delle persone attraverso un'analisi automatica delle loro attività sociali. Il lavoro proposto comprende un nuovo dataset, "Spectators Hockey" (S-Hock), dove vengono analizzate 4 partite di hockey su ghiaccio registrate in occasione di un torneo internazionale. Sui video ottenuti è stata effettuata una massiccia annotazione, con particolare attenzione verso gli spettatori a diversi livelli di dettaglio: ad alto livello, le persone sono state etichettate in base alla squadra che tifavano e in base al loro rapporto di conoscenza con la persona seduta a fianco; a basso livello invece sono state annotate informazioni relative alla posa (della testa e del corpo), ma anche azioni specifiche come battere le mani, sventolare bandiere ecc. L'annotazione si è focalizzata anche sul campo di gioco al fine di mettere in relazione il comportamento della folla con quello che avviene in campo. Questo lavoro ha portato a più di 100 milioni di annotazioni, utili per applicazioni standard di basso livello come il conteggio di oggetti, il rilevamento di persone e la stima della posa delle teste, ma anche per le applicazioni di alto livello, come la categorizzazione degli spettatori e il riconoscimento degli eventi. Per tutte queste applicazioni forniamo protocolli e baseline dei risultati al fine di favorire ulteriori ricerche. All'interno di questo quadro generale, l'obiettivo della tesi è duplice: da un lato, dimostrare come un forte background sociologico sia necessario per affrontare il problema generale dell'analisi delle folle; dall'altro, sottolineare la necessità di approfondire un problema specifico, come quello della folla di spettatori, attraverso la progettazione di metodi in grado di adattarsi alle peculiarità di uno scenario innovativo per la Computer Vision. Noi confidiamo sul fatto che S-Hock e i nostri studi possano innescare lo sviluppo di approcci innovativi ed efficaci per l'analisi del comportamento delle persone in ambienti affollati.
What this thesis proposes is a new type of crowd analysis in computer vision, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see". Typical scenarios of spectator crowds are stadiums, amphitheaters, classrooms, etc., and they share some aspects with classical crowd monitoring; for instance, since many people are simultaneously observed, per-person analysis is hard; however, in the considered cases, the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, watch, support/heckle the players or discuss with the neighbors. We start facing this challenge by following a social signal processing approach, which grounds computer vision techniques in social theories. More specifically, leveraging on social theories describing expressive bodily conduct, we will show interesting results on how it is possible to distinguish people behaviors by automatically detecting their social activities. In particular, we propose a novel dataset, the Spectators Hockey (S-Hock), which deals with 4 hockey matches recorded during an international tournament. A massive annotation has been carried out on the dataset, focusing on the spectators at different levels of detail: at a higher level, people have been labeled depending on the team they were supporting and on the acquaintance they have with spectators who sit close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body), but also fine grained actions such as hands on hips, clapping hands, etc. The labeling has also been focused on the game field, allowing to relate what was going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard lowlevel applications as object counting, people detection and head pose estimation, but also for high-level tasks, as spectator categorization and event recognition. For all of these we provide protocols and baseline results, encouraging further research. In this general picture, this thesis has been devoted to demonstrate that a strong sociological background is necessary to deal with crowd analysis in general, but also to underline the need to explore a novel specific issue, namely spectator crowd, by developing approaches able to adapt to the peculiarities of this scenario, which is new in computer vision. We are confident that S-Hock and our studies may trigger the design of novel and effective approaches for the analysis of human behavior in crowded settings and environments.

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Частини книг з теми "Crowd dataset"

Aksoy, Yağız, Changil Kim, Petr Kellnhofer, Sylvain Paris, Mohamed Elgharib, Marc Pollefeys, and Wojciech Matusik. "A Dataset of Flash and Ambient Illumination Pairs from the Crowd." In Computer Vision – ECCV 2018, 644–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_39.

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Cychnerski, Jan, and Tomasz Dziubich. "Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations." In New Trends in Database and Information Systems, 205–16. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85082-1_19.

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Rizvi, Syed Zeeshan, Muhammad Umar Farooq, and Rana Hammad Raza. "Performance Comparison of Deep Residual Networks-Based Super Resolution Algorithms Using Thermal Images: Case Study of Crowd Counting." In Digital Interaction and Machine Intelligence, 75–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11432-8_7.

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Анотація:

AbstractHumans are able to perceive objects only in the visible spectrum range which limits the perception abilities in poor weather or low illumination conditions. The limitations are usually handled through technological advancements in thermographic imaging. However, thermal cameras have poor spatial resolutions compared to RGB cameras. Super-resolution (SR) techniques are commonly used to improve the overall quality of low-resolution images. There has been a major shift of research among the Computer Vision researchers towards SR techniques particularly aimed for thermal images. This paper analyzes the performance of three deep learning-based state-of-the-art SR algorithms namely Enhanced Deep Super Resolution (EDSR), Residual Channel Attention Network (RCAN) and Residual Dense Network (RDN) on thermal images. The algorithms were trained from scratch for different upscaling factors of ×2 and ×4. The dataset was generated from two different thermal imaging sequences of BU-TIV benchmark. The sequences contain both sparse and highly dense type of crowds with a far field camera view. The trained models were then used to super-resolve unseen test images. The quantitative analysis of the test images was performed using common image quality metrics such as PSNR, SSIM and LPIPS, while qualitative analysis was provided by evaluating effectiveness of the algorithms for crowd counting application. After only 54 and 51 epochs of RCAN and RDN respectively, both approaches were able to output average scores of 37.878, 0.986, 0.0098 and 30.175, 0.945, 0.0636 for PSNR, SSIM and LPIPS respectively. The EDSR algorithm took the least computation time during both training and testing because of its simple architecture. This research proves that a reasonable accuracy can be achieved with fewer training epochs when an application-specific dataset is carefully selected.

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Favaretto, Rodolfo Migon, Soraia Raupp Musse, and Angelo Brandelli Costa. "Video Analysis Dataset and Applications." In Emotion, Personality and Cultural Aspects in Crowds, 127–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22078-5_10.

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Jingying, Wang. "A Survey on Crowd Counting Methods and Datasets." In Advances in Computer, Communication and Computational Sciences, 851–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4409-5_76.

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Pohlisch, Jakob. "Managing the Crowd: A Literature Review of Empirical Studies on Internal Crowdsourcing." In Contributions to Management Science, 27–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52881-2_3.

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Анотація:

AbstractThe phenomenon of crowdsourcing is increasingly being addressed in academic literature. Companies utilize crowdsourcing to search for solutions to internal problems outside of the companies’ boundaries, accessing the vast and diverse knowledge and creativity of people all over the world. More recently, a growing interest has emerged that concentrates on the intra-organizational application of this phenomenon—internal crowdsourcing. While conventional internal innovation activities are mostly concentrated within a few dedicated departments, this new approach helps companies to open up their innovation process to all employees. Internal crowdsourcing can help companies bridge geographical distances, integrate new employees, predict the market success of products, and create ideas for new businesses.This chapter aims to provide a comprehensive overview of the existing empirical findings regarding the management of internal crowdsourcing. In this review, 27 papers, covering more than 100 companies, are analysed. They are based on more than 800 interviews, participant observations, action design research, surveys, and datasets of internal innovation contests. The results of this review will help practitioners to design the management of internal crowdsourcing based on existing implementations and lessons learned, helping them to unleash the full innovation potential of their employees, creating a valuable competitive advantage.

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Alameda-Pineda, Xavier, Ramanathan Subramanian, Elisa Ricci, Oswald Lanz, and Nicu Sebe. "SALSA: A Multimodal Dataset for the Automated Analysis of Free-Standing Social Interactions." In Group and Crowd Behavior for Computer Vision, 321–40. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-809276-7.00017-5.

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ter Veen, James, Shahram Sarkani, and Thomas A. Mazzuchi. "Seeking an Online Social Media Radar." In Social Media and the Transformation of Interaction in Society, 67–92. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8556-7.ch005.

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In this paper we identify a method, which rapidly analyzes vast amounts of data present in social media in order to forecast crowd sizes. Based upon comparative analysis of related literature, a conceptual model is proposed and research conducted to develop capabilities to forecast mass collective action behavior such as crowd formation using Social Network Analysis (SNA) tools applied to online social media. We demonstrate that a simple model of online social network parameters can produce situation awareness of crowd sizes in much the same way that radar sensors can produce situation awareness of air traffic density. A prototype online social media ‘radar' sensor system is developed and tested in a pilot study with a dataset of tweets gathered regarding the Occupy Wall Street movement. Further work is suggested which could provide anticipated crowd location, movement and intent in addition to size.

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Zhong, Wencan, Vijayalakshmi G. V. Mahesh, Alex Noel Joseph Raj, and Nersisson Ruban. "Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks." In Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, 241–65. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6690-9.ch013.

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Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.

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Zhang, Hongyu, and Jacek Malczewski. "Quality Evaluation of Volunteered Geographic Information." In Crowdsourcing, 1173–201. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8362-2.ch058.

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A large amount of crowd-sourced geospatial data have been created in recent years due to the interactivity of Web 2.0 and the availability of Global Positioning System (GPS). This geo-information is typically referred to as volunteered geographic information (VGI). OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of quality of geo-information generated by OSM has become a trending research topic because of the large size of the dataset and the inapplicability of Linus' Law in a geospatial context. This chapter systematically reviews the quality evaluation process of OSM, and demonstrates a case study of London, Canada for the assessment of completeness, positional accuracy and attribute accuracy. The findings of the quality evaluation can potentially serve as a guide of cartographic product selection and provide a better understanding of the development of OSM quality over geographic space and time.

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Тези доповідей конференцій з теми "Crowd dataset"

Mahmoud, Samar, and Yasmine Arafaf. "Abnormal High-Density Crowd Dataset." In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA). IEEE, 2020. http://dx.doi.org/10.1109/mcna50957.2020.9264277.

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Dupont, Camille, Luis Tobias, and Bertrand Luvison. "Crowd-11: A Dataset for Fine Grained Crowd Behaviour Analysis." In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2017. http://dx.doi.org/10.1109/cvprw.2017.271.

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Schmuck, Viktor, and Oya Celiktutan. "RICA: Robocentric Indoor Crowd Analysis Dataset." In UKRAS20 Conference: “Robots into the real world”. EPSRC UK-RAS Network, 2020. http://dx.doi.org/10.31256/io1sq2r.

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Huang, Hun, Ge Gao, Ziyi Ke, Cheng Peng, and Ming Gu. "A Multi-Scenario Crowd Data Synthesis Based On Building Information Modeling." In The 29th EG-ICE International Workshop on Intelligent Computing in Engineering. EG-ICE, 2022. http://dx.doi.org/10.7146/aul.455.c223.

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Deep learning methods have proven to be effective in the field of crowd analysis recently. Nonetheless, the performance of deep learning models is affected by the inadequacy of training datasets. Because of policy implications and privacy restrictions, crowd data is commonly difficult to access. In order to overcome the difficulty of insufficient dataset, the previous work used to synthesize labelled crowd data in outdoor scenes and virtual games. However, these methods perform data synthesis with limited environmental information and inflexible crowd rules, usually in unauthentic environment. In this paper, a tool for synthesizing crowd data in BIM models with multiple scenes is proposed. This tool can make full use of the comprehensive information of real-world buildings, and conduct crowd simulations by setting behavior rules. The synthesized dataset is used for data augmentation for crowd analysis problems and the experimental results clearly confirm the effectiveness of the tool.

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Sohn, Samuel S., Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic, and Mubbasir Kapadia. "Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/185.

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With the rise in popularity of short-term Human Trajectory Prediction (HTP), Long-Term Crowd Flow Prediction (LTCFP) has been proposed to forecast crowd movement in large and complex environments. However, the input representations, models, and datasets for LTCFP are currently limited. To this end, we propose Fourier Isovists, a novel input representation based on egocentric visibility, which consistently improves all existing models. We also propose GeoInteractNet (GINet), which couples the layers between a multi-scale attention network (M-SCAN) and a convolutional encoder-decoder network (CED). M-SCAN approximates a super-resolution map of where humans are likely to interact on the way to their goals and produces multi-scale attention maps. The CED then uses these maps in either its encoder's inputs or its decoder's attention gates, which allows GINet to produce super-resolution predictions with substantially higher accuracy than existing models even with Fourier Isovists. In order to evaluate the scalability of models to large and complex environments, which the only existing LTCFP dataset is unsuitable for, a new synthetic crowd dataset with both real and synthetic environments has been generated. In its nascent state, LTCFP has much to gain from our key contributions. The Supplementary Materials, dataset, and code are available at sssohn.github.io/GeoInteractNet.

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Kiskin, Ivan, Adam D. Cobb, Lawrence Wang, and Stephen Roberts. "Humbug Zooniverse: A Crowd-Sourced Acoustic Mosquito Dataset." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053141.

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Schroder, Gregory, Tobias Senst, Erik Bochinski, and Thomas Sikora. "Optical Flow Dataset and Benchmark for Visual Crowd Analysis." In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018. http://dx.doi.org/10.1109/avss.2018.8639113.

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Kwon, Heeyoung, Mahnaz Koupaee, Pratyush Singh, Gargi Sawhney, Anmol Shukla, Keerthi Kumar Kallur, Nathanael Chambers, and Niranjan Balasubramanian. "Modeling Preconditions in Text with a Crowd-sourced Dataset." In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.340.

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Ma, Zhiheng, Xiaopeng Hong, Xing Wei, Yunfeng Qiu, and Yihong Gong. "Towards A Universal Model for Cross-Dataset Crowd Counting." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00319.

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Pan, Zhiwen, Shuangye Zhao, Jesus Pacheco, Yuxin Zhang, Xiaofan Song, Yiqiang Chen, Lianjun Dai, and Jun Zhang. "Comprehensive Data Management and Analytics for General Society Survey Dataset." In ICCSE'19: The 4th International Conference on Crowd Science and Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3371238.3371269.

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Звіти організацій з теми "Crowd dataset"

Anilkumar, Rahul, Benjamin Melone, Michael Patsula, Christopher Tran, Christopher Wang, Kevin Dick, Hoda Khalil, and G. A. Wainer. Canadian jobs amid a pandemic : examining the relationship between professional industry and salary to regional key performance indicators. Department of Systems and Computer Engineering, Carleton University, June 2022. http://dx.doi.org/10.22215/dsce/220608.

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The COVID-19 pandemic has contributed to massive rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a “snapshot” of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indicators (e.g. quality of life index, cost of living) for major cities across Canada. This analysis aims to help Canadians make informed career decisions, collect a “snapshot” of the Canadian employment opportunities amid a pandemic, and inform job seekers in identifying the correct fit between the desired lifestyle of a city and their career. We collected a new high-quality dataset of job postings from jobbank.gc.ca obtained with the use of ethical web scraping and performed exploratory data analysis on this dataset to identify job opportunity trends. When optimizing for average salary of job openings with quality of life, affordability, cost of living, and traffic indices, it was found that Edmonton, AB consistently scores higher than the mean, and is therefore an attractive place to move. Furthermore, we identified optimal provinces to relocate to with respect to individual skill levels. It was determined that Ajax, Marathon, and Chapleau, ON are each attractive cities for IT professionals, construction workers, and healthcare workers respectively when maximizing average salary. Finally, we publicly release our scraped dataset as a mid-pandemic snapshot of Canadian employment opportunities and present a public web application that provides an interactive visual interface that summarizes our findings for the general public and the broader research community.

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Lu, Tianjun, Jian-yu Ke, Fynnwin Prager, and Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, August 2022. http://dx.doi.org/10.31979/mti.2022.2147.

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Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced platform (i.e., Amazon Mechanical Turk) to obtain valid samples across California (n=1,985) and conducted state-level interviews among stakeholders (n=28) across ten major industries in California. The study leveraged secondary datasets and developed regression and time-series models. Our surveys found that, compared to pre-pandemic levels, more people had a dedicated workspace at home and had received adequate training and support for telecommuting, became more flexible to choose their own schedules, and had improved their working performance—but felt isolated and found it difficult to separate home and work life. Our interviews suggested that telecommuting policies were not commonly designed and implemented until COVID-19. Additionally, regression analyses showed that telecommuting practices have been influenced by COVID-19 related policies, public risk perception, home prices, broadband rates, and government employment. This study reveals advantages and disadvantages of telecommuting and unveils the complex relationships among the COVID-19 outbreak, transportation systems, employment, land use, and emissions as well as public risk perception and economic factors. The study informs statewide and regional policies to adapt to the new patterns of telecommuting.

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