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Опубліковано: 7 липня 2024

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Статті в журналах з теми "Dense crowd":

1

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.
2

Sam, Deepak Babu, Neeraj N. Sajjan, Himanshu Maurya, and R. Venkatesh Babu. "Almost Unsupervised Learning for Dense Crowd Counting." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8868–75. http://dx.doi.org/10.1609/aaai.v33i01.33018868.

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We present an unsupervised learning method for dense crowd count estimation. Marred by large variability in appearance of people and extreme overlap in crowds, enumerating people proves to be a difficult task even for humans. This implies creating large-scale annotated crowd data is expensive and directly takes a toll on the performance of existing CNN based counting models on account of small datasets. Motivated by these challenges, we develop Grid Winner-Take-All (GWTA) autoencoder to learn several layers of useful filters from unlabeled crowd images. Our GWTA approach divides a convolution layer spatially into a grid of cells. Within each cell, only the maximally activated neuron is allowed to update the filter. Almost 99.9% of the parameters of the proposed model are trained without any labeled data while the rest 0.1% are tuned with supervision. The model achieves superior results compared to other unsupervised methods and stays reasonably close to the accuracy of supervised baseline. Furthermore, we present comparisons and analyses regarding the quality of learned features across various models.
3

Zhang, Jin, Sheng Chen, Sen Tian, Wenan Gong, Guoshan Cai, and Ying Wang. "A Crowd Counting Framework Combining with Crowd Location." Journal of Advanced Transportation 2021 (February 17, 2021): 1–14. http://dx.doi.org/10.1155/2021/6664281.

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In the past ten years, crowd detection and counting have been applied in many fields such as station crowd statistics, urban safety prevention, and people flow statistics. However, obtaining accurate positions and improving the performance of crowd counting in dense scenes still face challenges, and it is worthwhile devoting much effort to this. In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. The first part is a fully convolutional neural network (CNN) consisting of backend and upsampling. In the first part, backend uses the residual network (ResNet) to encode the features of the input picture, and upsampling uses the deconvolution layer to decode the feature information. The first part processes the input image, and the processed image is input to the second part. The second part is a peak confidence map (PCM), which is proposed based on an improvement over the density map (DM). Compared with DM, PCM can not only solve the problem of crowd counting but also accurately predict the location of the person. The experimental results on several datasets (Beijing-BRT, Mall, Shanghai Tech, and UCF_CC_50 datasets) show that the proposed framework can achieve higher crowd counting performance in dense scenarios and can accurately predict the location of crowds.
4

Ma, Junjie, Yaping Dai, and Kaoru Hirota. "A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (March 15, 2017): 235–46. http://dx.doi.org/10.20965/jaciii.2017.p0235.

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Population growth has made the probability of incidents at large-scale crowd events higher than ever. In the past decades, automated crowd scene analysis done by computer vision has attracted attention. However, severe occlusions and complex crowd behaviors make such analysis a challenge. As a key aspect of crowd scene analysis, a number of works dealing with dense crowd anomaly detection based on computer vision have been presented. This work is a survey of computer vision techniques for analyzing dense crowd scenes. It covers two aspects: crowd density estimation and abnormal event detection. Some problems and perspectives are discussed at the end.
5

Miao, Yunqi, Zijia Lin, Guiguang Ding, and Jungong Han. "Shallow Feature Based Dense Attention Network for Crowd Counting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11765–72. http://dx.doi.org/10.1609/aaai.v34i07.6848.

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While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and human crowds generally have noticeably different responses in shallow features, we decide to build our attention model upon shallow-feature maps, which results in accurate background-pixel detection. Moreover, considering that the most representative features of people across different scales can appear in different layers of a feature extraction network, to better keep them all, we propose to densely connect hierarchical image features of different layers and subsequently encode them for estimating crowd density. Experimental results on three benchmark datasets clearly demonstrate the superiority of SDANet when dealing with different scenarios. Particularly, on the challenging UCF_CC_50 dataset, our method outperforms other existing methods by a large margin, as is evident from a remarkable 11.9% Mean Absolute Error (MAE) drop of our SDANet.
6

Huang, Liangjun, Shihui Shen, Luning Zhu, Qingxuan Shi, and Jianwei Zhang. "Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting." Sensors 22, no. 9 (April 22, 2022): 3233. http://dx.doi.org/10.3390/s22093233.

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In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.
7

Narain, Rahul, Abhinav Golas, Sean Curtis, and Ming C. Lin. "Aggregate dynamics for dense crowd simulation." ACM Transactions on Graphics 28, no. 5 (December 2009): 1–8. http://dx.doi.org/10.1145/1618452.1618468.

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8

Kok, Ven Jyn, and Chee Seng Chan. "Granular-based dense crowd density estimation." Multimedia Tools and Applications 77, no. 15 (December 5, 2017): 20227–46. http://dx.doi.org/10.1007/s11042-017-5418-y.

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9

Zhang, Jin, Luqin Ye, Jiajia Wu, Dan Sun, and Cheng Wu. "A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems." International Journal of Intelligent Systems 2023 (October 18, 2023): 1–13. http://dx.doi.org/10.1155/2023/6677622.

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Dense crowd counting has become an essential technology for urban security management. The traditional crowd counting methods mainly apply to the scene with a single view and obvious features but cannot solve the problem with a large area and fuzzy crowd features. Therefore, this paper proposes a crowd counting method based on high and low view information fusion (HLIF) for large and complex scenes. First, a neural network based on an attention mechanism (AMNet) is established to obtain a global density map from a high view and crowd counts from a low view. Then, the temporal correlation and spatial complementarity between cameras are used to calibrate the overlap areas of the two images. Finally, the total number of people is calculated by combining the low-view crowd counts and the high-view density map. Compared to single-view crowd counting methods, HLIF is experimentally more accurate and has been successfully applied in practice.
10

Aziz, Muhammad Waqar, Farhan Naeem, Muhammad Hamad Alizai, and Khan Bahadar Khan. "Automated Solutions for Crowd Size Estimation." Social Science Computer Review 36, no. 5 (September 11, 2017): 610–31. http://dx.doi.org/10.1177/0894439317726510.

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The crowd phenomenon frequently occurs in dense urban living environments. Crowd counting or estimation helps to develop management strategies such as designing safe public places and evacuation plan for emergencies. These strategies are different depending upon the type of event such as political and public demonstrations, sports, and religious events. However, estimating the number of people in crowds at closed or open environments is quite challenging because of the dynamics involved in the process. In addition, crowd estimation itself poses challenges due to randomness in crowd behavior, motion, and an area’s geometric specifications. Crowd behavior as well as the area parameters is studied before suggesting any possible technological solution for managing a crowd. This article presents a theoretical understanding of the major crowd size estimation approaches that cannot be achieved through the study of existing survey papers in this area, because the existing survey papers focus on particular technologies/specific areas with no or brief description of the involved steps. Besides, this article also highlights the strength and weakness of crowd size estimation solutions and their possible applications. It is, therefore, believed that the provided information would assist in developing an intelligent system for crowd management.
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Статті в журналах

Дисертації з теми "Dense crowd":

1

Vandoni, Jennifer. "Ensemble Methods for Pedestrian Detection in Dense Crowds." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS116/document.

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Cette thèse s’intéresse à la détection des piétons dans des foules très denses depuis un système mono-camera, avec comme but d’obtenir des détections localisées de toutes les personnes. Ces détections peuvent être utilisées soit pour obtenir une estimation robuste de la densité, soit pour initialiser un algorithme de suivi. Les méthodologies classiques utilisées pour la détection de piétons s’adaptent mal au cas où seulement les têtes sont visibles, de part l’absence d’arrière-plan, l’homogénéité visuelle de la foule, la petite taille des objets et la présence d’occultations très fortes. En présence de problèmes difficiles tels que notre application, les approches à base d’apprentissage supervisé sont bien adaptées. Nous considérons un système à plusieurs classifieurs (Multiple Classifier System, MCS), composé de deux ensembles différents, le premier basé sur les classifieurs SVM (SVM- ensemble) et le deuxième basé sur les CNN (CNN-ensemble), combinés dans le cadre de la Théorie des Fonctions de Croyance (TFC). L’ensemble SVM est composé de plusieurs SVM exploitant les données issues d’un descripteur différent. La TFC nous permet de prendre en compte une valeur d’imprécision supposée correspondre soit à une imprécision dans la procédure de calibration, soit à une imprécision spatiale. Cependant, le manque de données labellisées pour le cas des foules très denses nuit à la génération d’ensembles de données d’entrainement et de validation robustes. Nous avons proposé un algorithme d’apprentissage actif de type Query-by- Committee (QBC) qui permet de sélectionner automatiquement de nouveaux échantillons d’apprentissage. Cet algorithme s’appuie sur des mesures évidentielles déduites des fonctions de croyance. Pour le second ensemble, pour exploiter les avancées de l’apprentissage profond, nous avons reformulé notre problème comme une tâche de segmentation en soft labels. Une architecture entièrement convolutionelle a été conçue pour détecter les petits objets grâce à des convolutions dilatées. Nous nous sommes appuyés sur la technique du dropout pour obtenir un ensemble CNN capable d’évaluer la fiabilité sur les prédictions du réseau lors de l’inférence. Les réalisations de cet ensemble sont ensuite combinées dans le cadre de la TFC. Pour conclure, nous montrons que la sortie du MCS peut être utile aussi pour le comptage de personnes. Nous avons proposé une méthodologie d’évaluation multi-échelle, très utile pour la communauté de modélisation car elle lie incertitude (probabilité d’erreur) et imprécision sur les valeurs de densité estimées
This study deals with pedestrian detection in high- density crowds from a mono-camera system. The detections can be then used both to obtain robust density estimation, and to initialize a tracking algorithm. One of the most difficult challenges is that usual pedestrian detection methodologies do not scale well to high-density crowds, for reasons such as absence of background, high visual homogeneity, small size of the objects, and heavy occlusions. We cast the detection problem as a Multiple Classifier System (MCS), composed by two different ensembles of classifiers, the first one based on SVM (SVM-ensemble) and the second one based on CNN (CNN-ensemble), combined relying on the Belief Function Theory (BFT) to exploit their strengths for pixel-wise classification. SVM-ensemble is composed by several SVM detectors based on different gradient, texture and orientation descriptors, able to tackle the problem from different perspectives. BFT allows us to take into account the imprecision in addition to the uncertainty value provided by each classifier, which we consider coming from possible errors in the calibration procedure and from pixel neighbor's heterogeneity in the image space. However, scarcity of labeled data for specific dense crowd contexts reflects in the impossibility to obtain robust training and validation sets. By exploiting belief functions directly derived from the classifiers' combination, we propose an evidential Query-by-Committee (QBC) active learning algorithm to automatically select the most informative training samples. On the other side, we explore deep learning techniques by casting the problem as a segmentation task with soft labels, with a fully convolutional network designed to recover small objects thanks to a tailored use of dilated convolutions. In order to obtain a pixel-wise measure of reliability about the network's predictions, we create a CNN- ensemble by means of dropout at inference time, and we combine the different obtained realizations in the context of BFT. Finally, we show that the output map given by the MCS can be employed to perform people counting. We propose an evaluation method that can be applied at every scale, providing also uncertainty bounds on the estimated density
2

Chatagnon, Thomas. "Standing balance recovery strategies following external perturbations : a multiscale approach with applications to dense crowds." Electronic Thesis or Diss., Université de Rennes (2023-....), 2023. http://www.theses.fr/2023URENE007.

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Chaque année dans le monde on rapporte plusieurs accidents liés aux foules denses. Ces accidents peuvent entraîner des blessures et même des décès dans les cas les plus extrêmes. Les chutes dans les foules denses ont été observées comme l’un des éléments déclencheurs possibles de ces accidents. De telles chutes résultent des difficultés de recouvrement de l’équilibre postural dans ce contexte particulier. Cependant, les connaissances concernant l’équilibre postural dans ce contexte sont très limitées en raison de la nature complexe des environnements liés aux foules denses. Dans cette thèse, nous proposons une approche expérimentale pour étudier le rétablissement d’équilibre postural suite à des perturbations externes dans des environnements de foules humaines avec différents niveaux de densité. En particulier, comme les perturbations peuvent survenir de n’importe quelle direction dans les foules, nous étudions dans un premier temps la réaction d’individus isolés à des perturbations externes provenant de plusieurs directions. Nous proposons ensuite une méthode permettant de prédire les stratégies de recouvrement d’équilibre des participants sur la base d’informations cinématiques uniquement. Des expériences impliquant plusieurs individus sont par la suite étudiées. En particulier, nous avons étudié l’effet de la densité de la foule sur l’initiation et les caractéristiques des pas de récupération d’équilibre. Finalement, un nouveau paradigme expérimental a été proposé pour étudier l’équilibre postural en conditions réelles lors d’événements impliquant des foules denses. Cette approche a ensuite été utilisée au sein de foules durant des concerts de musique punk rock. Les résultats fournissent de nouvelles informations concernant la nature des interactions physiques et les stratégies de récupération d’équilibre utilisées dans ce contexte
Every year, several crowd accidents are reported around the world. These accidents can result in injuries and even fatalities. One of the triggering element of these accidents has been reported to result from falls associated with loss of standing balance in this dense crowd context. However, knowledge regarding human standing balance in dense crowd is very limited due the complex nature of this environment. In this thesis we proposed an experimental approach to study standing balance recovery of individuals following external perturbations in environments with different crowd density. In particular, as perturbation may occur from any direction in dense crowds we first study the reaction to single individuals to external perturbations arising from different directions. Based on the data collected during of this experiment, we were able to propose a method to predict participants’ recovery strategies following external perturbations based on kinematic information. Experiment involving multiple individuals were then considered. In particular, we study the effect of the crowd density on the initiation and the characteristics of recovery steps. Eventually, a novel experimental paradigm was proposed to study standing balance during real crowded events. This approach was then used within punk rock concert crowds. Results provided new insights regarding the physical interactions and recovery strategies observed in this context
3

Filho, Antonio Ruy Chaves. ""Estudo comparativo da resistência às forças de compressão de uma coroa metalocerâmica fraturada e reparada com resina composta híbrida fotopolimerizável com um dente natural permanente e com uma coroa metalocerâmica íntegra"." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/23/23134/tde-25062002-100754/.

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As porcelanas odontológicas têm sido dos materiais restauradores estéticos aqueles que mais têm evoluído nos últimos anos. Tradicionalmente, elas são friáveis e incapazes de suportar deformações estruturais quando submetidas a stress. Quando usadas na construção de próteses parciais fixas, uma subestrutura metálica é necessária para impedir que se fraturem sob esforços oclusais. Forças de compressão aplicadas em uma porcelana suportada por metal, de tal forma que não seja possível a sua flexão, poderão ser suportadas em elevadas magnitudes. Entretanto, falhas podem acontecer manifestando-se normalmente por fraturas que exigem a intervenção do clínico no sentido de corrigi-las. Diversos são os métodos propostos para o reparo destas fraturas e as pesquisas desenvolvidas no sentido de se avaliar a união dos materiais restauradores às porcelanas. Neste trabalho, foi feita uma análise das possíveis causas de fratura das cerâmicas, os métodos de reparo indicados, materiais empregados para tal e a eficiência dos sistemas indicados frente às variáveis que envolvem o procedimento restaurador da porcelana. A proposta foi avaliar a resistência máxima às forças de compressão de reparos de fraturas de porcelana realizados com uma resina composta híbrida fotopolimerizável, empregando cargas estáticas comparando-a com a resistência de coroas metalocerâmicas íntegras e dentes naturais permanentes, usados como grupo controle. Os resultados mostraram que frente às forças de compressão aplicadas, a coroa metalocerâmica íntegra e o dente natural não apresentaram diferença estatisticamente significante. A coroa metalocerâmica reparada apresentou menor resistência que a coroa
The odontologic porcelains have been out of the esthetic restorative materials those, which have developed the most in the last years. Traditionally, they are brittle and unable to bear structural deformations when submitted to stress. When used in the building of partial fixed prothesis, one metallic framework is needed to avoid them to fracture under occlusal efforts. Forces of compression enforced on a metal supported porcelain in a way, which is not possible to bend, they could be beared in high magnitudes. However, failures can happen normally revealing themselves through fractures, which demand the intervention of the clinician in order to evaluate the association of the restorative materials to porcelains. In this study na analysis has been made of the possible causes of fractures of the ceramics, the indicated repairing methods, the materials used for it and, the efficiency of the variables, which involves the restorative procedure of the porcelain. The aim was evaluate the utmost resistance to the forces of compression in repairs of fractures of porcelain made with a light-cured hybrid composite resin, using static loads and comparing it with the resistance of the nonfractured metal ceramic crowns and permanent natural human teeth, used as control group The results have shown that in the presence of the compression forces loaded to non-fractured metaloceramic crown and the natural tooth have not exhibited statistically meaningful difference. The repaired metaloceramic crown has exhibited less resistance than the non-fractured metaloceramic one. However, there has not been statistically meaningful difference to the permanent natural tooth
4

Andrade, Guilherme Schmitt de. "Influência da presença e do tipo de retentor intrarradicular no comportamento em fadiga de incisivos centrais restaurados com coroas totais /." São José dos Campos, 2019. http://hdl.handle.net/11449/180531.

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Orientador: Guilherme de Siqueira Ferreira Anzaloni Saavedra
Coorientador: Lilian Costa Anami
Banca: Carlos Eduardo Francci
Banca: Clovis Pagani
Resumo: Este estudo avalia a influência da presença e tipo de pinos e núcleos na sobrevivência à fadiga de incisivos centrais restaurados com coroas totais. As coroas de 69 incisivos bovinos foram cortadas e as raízes tratadas endodonticamente. As raízes foram distribuídas aleatoriamente em três grupos (n = 23): núcleo de resina composta (NRC), núcleo de resina composta retido por pino de fibra de vidro (PFV) e núcleo metálico fundido (NMF). Todos receberam preparo de coroa total com férula de 2 mm, e então, uma coroa de cerâmica reforçada com leucita foi adesivamente cimentada. Três amostras de cada grupo foram testadas para determinação da carga máxima até a falha. As amostras restantes foram carregadas pelo teste acelerado de fadiga stepwise stress até a fratura ou suspensão após o término de 1,5 x 106 ciclos. A carga e o degrau em que cada espécime falhou foram analisadas pela estatística de Kaplan-Meier e Mantel-Cox (Log Rank test), seguidas por comparação múltipla aos pares, com nível de significância de 5%. O modo de falha foi analisado com estéreomicrocópio. Não houve diferença estatística entre os tratamentos quanto à carga (Mantel-Cox Log-Rank test for trend, X2=0,015, df=1, p=0,901), nem para o número de ciclos (Mantel-Cox Log-Rank test for trend, X2 =3,171, df=1, p=0,995). O modo de falha predominante foi a trinca da coroa. Fratura obliqua da raiz só foi observado nos grupos retidos por pinos. Nos incisivos tratados endodonticamente com férula de 2 mm, a presença e o tipo... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract : This study evaluates the influence of presence and type of post-and-core systems on fatigue survival of non-vital central incisors restored with all-ceramic crowns. The crowns of 69 bovine incisors were cut and endodontically treated. The roots were randomly assigned into three groups (n=23): composite resin build-up (NRC), glass fiber post-retained-composite build-up (PFV) and cast post-and-core (NMF). All specimens received crown preparation with a 2 mm ferrule, and then a leucite-reinforced ceramic crown was adhesively cemented. Three samples from each group were tested for the determination of the maximum load to failure. The remaining samples were loaded by the accelerated fatigue stepwise stress test until fracture or suspension after the end of 1.5 x 106 cycles. The load and the step on which each specimen failed were analyzed by the Kaplan-Meier and Mantel-Cox (Log Rank test) statistics, followed by multiple paired comparisons, with a significance level of 5%. The failure mode was analyzed with stereomicroscope. There was no statistically significant difference between the treatments (Mantel-Cox Log-Rank test for trend, X2=0.015, df=1, p=0.901), nor for the number of cycles (Mantel-Cox Log-Rank test for trend, X2=3.171, df=1, p=0.995). The predominant failure mode was the crown crack. Oblique root fracture was observed only in groups retained by posts. The presence and type of post system did not influence the fatigue survival of the endodontically treated incisors with a 2 mm ferrule. Non-restorable fractures only occurred on teeth restored with posts...(Complete abstract click electronic access below)
Mestre
5

Rocha, Isaac José Peixoto Batinga da. "Estudo das tensões em dente restaurado com coroa metalocerâmica e dois formatos de retentores intra-radiculares - método dos elementos finitos." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/23/23137/tde-25012001-154641/.

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Анотація:
O estudo comparativo da distribuição de tensões de von Mises em dente endodonticamente tratado, com dois formatos de retentores intra-radiculares, pré-fabricado cilíndrico/escalonado e fundido cônico, foi realizado pelo método dos elementos finitos em modelos tridimensionais de caninos superiores com geometria axi-simétrica e restaurados com coroas metalocerâmicas, aplicando-se uma carga oblíqua de 100N com 45 º de inclinação em relação ao seu longo eixo. Os resultados indicaram uma discreta diferença nos dois modelos (formatos) de retentores. O pino escalonado neutralizou mais as cargas no sentido corono-apical e no pino cônico ficou evidenciado o efeito de cunha.
The aim of this study was to compare the von Mises stress distribution on a human canine teeth endodontically treated and restored with two types of post and core. The analysis was made using the three dimensional Finite Element Analysis method. For the analysis was created and axy-simetric structure of the teeth, including a porcelain fused to metal crown, a cement layer, and the periodontal structures. The model was analyzed when a 100N load was applied on the surface of the crown with 45º angulation with the long axis of the teeth. The results showed that both models exhibit little difference, the “stair shape” post system neutralized the axial loads, and the conical shape post showed a wedge effect on the apical third of the root.
6

Huang, Hsing-Lu, and 黃星陸. "People localization and height determination for dense crowds using multiple cameras." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/99373121073131806114.

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Анотація:
碩士
國立交通大學
多媒體工程研究所
99
In recent years, researchers have been paying much attention on people tracking and localization using multiple cameras. Most of methods require a large number of computations to cope with serious occlusions, and need to rely on special hardware to achieve real-time locating and tracking. Unlike these studies, we use two dimension line samplings of foregrounds to restrict regions of possible locations of people. According to the nature of occupancy constraint that possible locations of people should be covered by more projected foreground pixels, we propose a random algorithm to efficiently find the locations and number of people. Our methods can provide real-time location results because of the smaller of searching space from line sampling and less computing time due to the randomness nature. Besides, we exploit the concept of people volume so that uniformly sampling on the ground plane can prevent error from occlusions. Experimental results show that our approach provides real-time and accurate people localization results under serious occlusions.
7

Trautman, Peter. "Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation." Thesis, 2013. https://thesis.library.caltech.edu/7724/7/caltech_thesis_pete_trautman.pdf.

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This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m2, while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m2. We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

8

Yen, Li-Fang, and 嚴立芳. "How the Celebrities of Entertainment use Social Media to Mediate Image Construction? - The case Study of Denise Ho and Crowd Lu." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/11729005087622914702.

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Анотація:
碩士
銘傳大學
傳播管理學系碩士班
99
This thesis focuses on the outcome of the celebrities taking initiative in using social media, specifically using Denise Ho and Guan-Chong Lu as the case studies. Due to the rapid growth in new media, the celebrities also actively utilize media platforms like Facebook, Microblog, etc. This study also sought to answer the following questions: 1) What are their motives? 2) Is the self-disclosure by the celebrities in these social media similar to their image on stage? 3) Does their behavior vary as they’re using different media? 　　This thesis is based on Goffman’s dramaturgical theory and Rogers’ use and gratifications theory . The study is adopted both of quantitative and qualitative methods to find out what the image difference between ‘on stage’ and ‘out of stage’ of the celebrities. After online survey by 774 of people from 2011/3/26 to 2011/4/7,and semi-structured in-depth interview of 15 people who is the fan of Denise Ho or Crowd Lu. 　　The preliminary result was that most of participant considers that they are rationality, and both of online survey and participants indicated that when the celebrities use social media by themselves, it can reduce estrangement from people, also it can win the people’s confidence than news media.

Книги з теми "Dense crowd":

1

Ellen, Snodgrass Mary. CliffsNotes American Poets of the 20th Century. New York: John Wiley & Sons, Ltd., 2000.

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2

Pub, Denise Personalized Journal. Denise : PRINCESS Denise. Unique Personalized Notebook Gift for Denise - Golden Crown Design , Thoughtful Cool Present for Denise: Princess Denise's Journal. Independently Published, 2020.

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3

Ellen, Snodgrass Mary. American Poets of the 20th Century (Cliffs Notes). Cliffs Notes, 2000.

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Частини книг з теми "Dense crowd":

1

Behera, Shreetam, Debi Prosad Dogra, and Partha Pratim Roy. "Characterization of Dense Crowd Using Gibbs Entropy." In Proceedings of 2nd International Conference on Computer Vision & Image Processing, 289–300. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7898-9_24.

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2

Shen, Liwen, Zhao Qiu, Ping Huang, Yu Jin, Chao Li, and Jinye Cai. "A Multi-scale Fusion Method for Dense Crowd Counting." In Advances in Artificial Intelligence and Security, 305–14. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_27.

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3

Ganesh, Nakkala, Vibhor Kedawat, and Jeetashree Aparajeet. "Automated Crowd Size Estimation in Dense Crowd Images—Application in Detecting COVID-19 Guideline Violations." In Cognitive Informatics and Soft Computing, 531–37. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8763-1_43.

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4

Dutta, Saikat, Soumya Kanti Naskar, Sanjoy Kumar Saha, and Bhabatosh Chanda. "Robust Identification of Dense or Sparse Crowd Based on Classifier Fusion." In Lecture Notes in Computer Science, 131–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34869-4_15.

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5

Sirisha, D., S. Sambhu Prasad, and Subodh Kumar. "Computer Vision and Convolutional Neural Network for Dense Crowd Count Detection." In Artificial Intelligence: Theory and Applications, 353–62. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8479-4_26.

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6

Xiong, Muzhou, Yunliang Chen, Hao Wang, and Min Hu. "An Agent-Based Model for Simulating Human-Like Crowd in Dense Places." In Communications in Computer and Information Science, 8–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_2.

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7

Zhang, Yu, Huaming Chen, Zhongzheng Lai, Zao Zhang, and Dong Yuan. "Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads." In Lecture Notes in Computer Science, 79–90. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8388-9_7.

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8

Butano, Matteo, Thibault Bonnemain, Cécile Appert-Rolland, Alexandre Nicolas, and Denis Ullmo. "Modeling of Obstacle Avoidance by a Dense Crowd as a Mean-Field Game." In Lecture Notes in Civil Engineering, 93–100. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7976-9_12.

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9

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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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.
10

Gitahi, Joseph, and Michael Hahn. "Evaluation of Crowd-Sourced PM2.5 Measurements from Low-Cost Sensors for Air Quality Mapping in Stuttgart City." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 225–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_14.

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AbstractExposure to particulate matter (PM) pollution poses a major risk to the environment and human health. Monitoring PM pollution is thus crucial to understand particle distribution and mitigation. There has been rapid development of low-cost PM sensors and advancement in the field of Internet of Things (IoT) that has led to the deployment of the sensors by technology-aware people in cities. In this study, we evaluate the stability and accuracy of PM measurements from low-cost sensors crowd-sourced from a citizen science project in Stuttgart. Long-term measurements from the sensors show a strong correlation with measurements from reference stations with most of the selected sensors achieving Pearson correlation coefficients of r > 0.7. We investigate the stability of the sensors for reproducibility of measurements using five sensors installed at different height levels and horizontal distances. They exhibit minor variations with low correlation of variation (CV) values of between 10 and 14%. A CV of ≤10% is recommended for low-cost sensors. In a dense network, the sensors enable extraction pollution patterns and trends. We analyse PM measurements from 2 years using space-time pattern analysis and generate two clusters of sensors that have similar trends. The clustering shows the relationship between traffic and pollution with most sensors near major roads being in the same cluster.

Тези доповідей конференцій з теми "Dense crowd":

1

Yang, Ge, and Siping Chen. "Pedestrian Detection Under Dense Crowd." In 2018 5th International Conference on Systems and Informatics (ICSAI). IEEE, 2018. http://dx.doi.org/10.1109/icsai.2018.8599382.

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2

Narain, Rahul, Abhinav Golas, Sean Curtis, and Ming C. Lin. "Aggregate dynamics for dense crowd simulation." In ACM SIGGRAPH Asia 2009 papers. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1661412.1618468.

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3

Roldao Reis, Victor Hugo, Silvio Jamil F. Guimaraes, and Zenilton Kleber Goncalves do Patrocinio. "Dense Crowd Counting with Capsule Networks." In 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2020. http://dx.doi.org/10.1109/iwssip48289.2020.9145163.

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4

Dai, Feng, Hao Liu, Yike Ma, Xi Zhang, and Qiang Zhao. "Dense Scale Network for Crowd Counting." In ICMR '21: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460426.3463628.

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5

Yang, Ge, and Zihao Chen. "Visual Surveillance Algorithm for Dense Crowd." In AICS 2019: 2019 International Conference on Artificial Intelligence and Computer Science. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3349341.3349485.

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6

Khokher, Muhammad Rizwan, Abdesselam Bouzerdoum, and Son Lam Phung. "Crowd Behavior Recognition Using Dense Trajectories." In 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2014. http://dx.doi.org/10.1109/dicta.2014.7008098.

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7

Sundararaman, Ramana, Cedric De Almeida Braga, Eric Marchand, and Julien Pettre. "Tracking Pedestrian Heads in Dense Crowd." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00386.

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8

Wang, Yang, Shouqiang Liu, Mingyue Jiang, Liming Chen, Jianming Zeng, and Wanggan Yang. "Dense Crowd Counting Based on ResNet." In 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS). IEEE, 2021. http://dx.doi.org/10.1109/ccis53392.2021.9754656.

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9

Li, Zirui, Yuexian Zou, Guoshuai Wang, and Jian Zhang. "Scale-Informed Density Estimation for Dense Crowd Counting." In 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019. http://dx.doi.org/10.1109/vcip47243.2019.8965799.

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10

Nasir, Fawwaz Mohd, Tsukasa Noma, Masaki Oshita, Kunio Yamamoto, Mohd Shahrizal Sunar, Shamsul Mohamad, and Yasutaka Honda. "Simulating group formation and behaviour in dense crowd." In VRCAI '16: The 15th International Conference on Virtual-Reality Continuum and its Applications in Industry. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3013971.3014017.

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