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Автор: Grafiati
Опубліковано: 7 липня 2024

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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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Анотація:
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.
11

Ilyas, Naveed, Boreom Lee, and Kiseon Kim. "HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting." Sensors 21, no. 10 (May 17, 2021): 3483. http://dx.doi.org/10.3390/s21103483.

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Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques.
12

Zhang, Wei, Yongjie Wang, Yanyan Liu, and Jianghua Zhu. "Deep convolution network for dense crowd counting." IET Image Processing 14, no. 4 (March 27, 2020): 621–27. http://dx.doi.org/10.1049/iet-ipr.2019.0435.

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13

Han, Suyu. "Hybrid Attention Fusion in Dense Crowd Counting." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 23, 2022): 35–38. http://dx.doi.org/10.54097/fcis.v2i1.2707.

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One of appealing approaches to guiding deep parameter optimization, is attentional supervision, which inspires intelligence in complex networks at a fraction of the cost, but there is still room for improvement. First, the real dense scene with varying scales and uneven density distribution of human heads, the density map cannot be clearly expressed. Second, the heavily occluded areas are extremely similar to the complex background, which further aggravates the counting error. Therefore, we propose a dual-track attention network that distinguishes between global and local information, which is responsible for the target overlap and background confusion problems, respectively, and finally converges and normalizes with the feature map to transform the multi-channel attention map into a single-channel density map. Meanwhile the heterogeneous pyramid design alleviates the distress of scale variation and density dissimilarity. Experiments on several official datasets prove the effectiveness of the scheme to enhance key information and overcome confounding factors.
14

Dang, Huu-Tu, Benoit Gaudou, and Nicolas Verstaevel. "A literature review of dense crowd simulation." Simulation Modelling Practice and Theory 134 (July 2024): 102955. http://dx.doi.org/10.1016/j.simpat.2024.102955.

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15

Kleinmeier, Benedikt, Gerta Köster, and John Drury. "Agent-based simulation of collective cooperation: from experiment to model." Journal of The Royal Society Interface 17, no. 171 (October 2020): 20200396. http://dx.doi.org/10.1098/rsif.2020.0396.

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Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically, crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypotheses on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents’ perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents’ ability to successfully get through a dense crowd emerges as an effect of the psychological model.
16

Zeng, Hui, Rong Hu, Xiaohui Huang, and Zhiying Peng. "Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning." Mathematical Problems in Engineering 2021 (September 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/7114981.

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Finding a feasible, collision-free path in line with social activities is an important and challenging task for robots working in dense crowds. In recent years, many studies have used deep reinforcement learning techniques to solve this problem. In particular, it is necessary to find an efficient path in a short time which often requires predicting the interaction with neighboring agents. However, as the crowd grows and the scene becomes more and more complex, researchers usually simplify the problem to a one-way human-robot interaction problem. But, in fact, we have to consider not only the interaction between humans and robots but also the influence of human-human interactions on the movement trajectory of the robot. Therefore, this article proposes a method based on deep reinforcement learning to enable the robot to avoid obstacles in the crowd and navigate smoothly from the starting point to the target point. We use a dual social attention mechanism to jointly model human-robot and human-human interaction. All sorts of experiments demonstrate that our model can make robots navigate in dense crowds more efficiently compared with other algorithms.
17

Qiu, Xiangfeng, Jin Ye, Siyu Chen, and Jinhe Su. "Hierarchical Inverse Distance Transformer for Enhanced Localization in Dense Crowds." Electronics 13, no. 12 (June 11, 2024): 2289. http://dx.doi.org/10.3390/electronics13122289.

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Achieving precise individual localization within densely crowded scenes poses a significant challenge due to the intricate interplay of occlusions and varying density patterns. Traditional methods for crowd localization often rely on convolutional neural networks (CNNs) to generate density maps. However, these approaches are prone to inaccuracies stemming from the extensive overlaps inherent in dense populations. To overcome this challenge, our study introduces the Hierarchical Inverse Distance Transformer (HIDT), a novel framework that harnesses the multi-scale global receptive fields of Pyramid Vision Transformers. By adapting to the multi-scale characteristics of crowds, HIDT significantly enhances the accuracy of individual localization. Incorporating Focal Inverse Distance techniques, HIDT adeptly addresses issues related to scale variation and dense overlaps, prioritizing local small-scale features within the broader contextual understanding of the scene. Rigorous evaluation on standardized benchmarks has unequivocally validated the superiority of our approach. HIDT exhibits outstanding performance across various datasets. Notably, on the JHU-Crowd++ dataset, our method demonstrates significant improvements over the baseline, with MAE and MSE metrics decreasing from 66.6 and 253.6 to 59.1 and 243.5, respectively. Similarly, on the UCF-QNRF dataset, performance metrics increase from 89.0 and 153.5 to 83.6 and 138.7, highlighting the effectiveness and versatility of our approach.
18

Li, Pengfei, Min Zhang, Jian Wan, and Ming Jiang. "Multiscale Aggregate Networks with Dense Connections for Crowd Counting." Computational Intelligence and Neuroscience 2021 (November 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/9996232.

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The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.
19

Gong, Shengrong, Shan Zhong, and Ran Yan. "Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images." International Journal of Computer Applications in Technology 61, no. 4 (2019): 318. http://dx.doi.org/10.1504/ijcat.2019.10024872.

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20

Yan, Ran, Shengrong Gong, and Shan Zhong. "Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images." International Journal of Computer Applications in Technology 61, no. 4 (2019): 318. http://dx.doi.org/10.1504/ijcat.2019.103298.

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21

Ma, Zhiheng, Xing Wei, Xiaopeng Hong, Hui Lin, Yunfeng Qiu, and Yihong Gong. "Learning to Count via Unbalanced Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2319–27. http://dx.doi.org/10.1609/aaai.v35i3.16332.

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Counting dense crowds through computer vision technology has attracted widespread attention. Most crowd counting datasets use point annotations. In this paper, we formulate crowd counting as a measure regression problem to minimize the distance between two measures with different supports and unequal total mass. Specifically, we adopt the unbalanced optimal transport distance, which remains stable under spatial perturbations, to quantify the discrepancy between predicted density maps and point annotations. An efficient optimization algorithm based on the regularized semi-dual formulation of UOT is introduced, which alternatively learns the optimal transportation and optimizes the density regressor. The quantitative and qualitative results illustrate that our method achieves state-of-the-art counting and localization performance.
22

Fagette, Antoine, Nicolas Courty, Daniel Racoceanu, and Jean-Yves Dufour. "Unsupervised dense crowd detection by multiscale texture analysis." Pattern Recognition Letters 44 (July 2014): 126–33. http://dx.doi.org/10.1016/j.patrec.2013.09.020.

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23

Eshel, Ran, and Yael Moses. "Tracking in a Dense Crowd Using Multiple Cameras." International Journal of Computer Vision 88, no. 1 (November 17, 2009): 129–43. http://dx.doi.org/10.1007/s11263-009-0307-0.

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24

Meng, Xiaolong. "SENetCount: An Optimized Encoder-Decoder Architecture with Squeeze-and-Excitation for Crowd Counting." Wireless Communications and Mobile Computing 2022 (June 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/2964683.

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Crowd management is critical to preventing stampedes and directing crowds, especially in India and China, where there are more than one billion people. With the continuous growth of the population, crowded events caused by rallies, parades, tourism, and other reasons occur from time to time. Crowd count estimation is the linchpin of the crowd management system and has become an increasingly important task and challenging research direction. This work proposes an optimized encoder-decoder architecture with the squeeze-and-excitation block for crowd counting, called SENetCount, which includes SE-ResNetCount and SE-ResNeXtCount. The deeper and stronger backbone network increases the quality of feature representations. The squeeze-and-excitation block utilizes global information to impress worthy informative feature representations and suppress unworthy ones selectively. The encoder-decoder architecture with the dense atrous spatial pyramid pooling module recovers the spatial information and captures the contextual information at multiple scales. The modified loss function considers the local consistency measure compared with the foregoing Euclidean loss function. The experiments on challenging datasets prove that our approach is competitive compared to thoughtful approaches, and analyses show that our architecture is extensible and robust.
25

Sato, Yuta, Yoko Sasaki, and Hiroshi Takemura. "STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds." PeerJ Computer Science 9 (October 30, 2023): e1641. http://dx.doi.org/10.7717/peerj-cs.1641.

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This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians’ future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning’s arrival times is 26.4% faster than conventional 2D path planning’s arrival time in a simulation of navigation in a crowd of 50 people.
26

Wong, Vivian W. H., and Kincho H. Law. "Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces." Algorithms 16, no. 3 (March 10, 2023): 154. http://dx.doi.org/10.3390/a16030154.

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Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards.
27

Zhang, Yulin, and Zhengyong Feng. "Crowd-Aware Mobile Robot Navigation Based on Improved Decentralized Structured RNN via Deep Reinforcement Learning." Sensors 23, no. 4 (February 6, 2023): 1810. http://dx.doi.org/10.3390/s23041810.

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Efficient navigation in a socially compliant manner is an important and challenging task for robots working in dynamic dense crowd environments. With the development of artificial intelligence, deep reinforcement learning techniques have been widely used in the robot navigation. Previous model-free reinforcement learning methods only considered the interactions between robot and humans, not the interactions between humans and humans. To improve this, we propose a decentralized structured RNN network with coarse-grained local maps (LM-SRNN). It is capable of modeling not only Robot–Human interactions through spatio-temporal graphs, but also Human–Human interactions through coarse-grained local maps. Our model captures current crowd interactions and also records past interactions, which enables robots to plan safer paths. Experimental results show that our model is able to navigate efficiently in dense crowd environments, outperforming state-of-the-art methods.
28

Liu, Yan-Bo, Rui-Sheng Jia, Jin-Tao Yu, Ruo-Nan Yin, and Hong-Mei Sun. "Crowd density estimation via a multichannel dense grouping network." Neurocomputing 449 (August 2021): 61–70. http://dx.doi.org/10.1016/j.neucom.2021.03.078.

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29

Metivet, T., L. Pastorello, and P. Peyla. "How to push one's way through a dense crowd." EPL (Europhysics Letters) 121, no. 5 (March 1, 2018): 54003. http://dx.doi.org/10.1209/0295-5075/121/54003.

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30

Zhang, Yanhao, Qingming Huang, Lei Qin, Sicheng Zhao, Hongxun Yao, and Pengfei Xu. "Representing dense crowd patterns using bag of trajectory graphs." Signal, Image and Video Processing 8, S1 (July 22, 2014): 173–81. http://dx.doi.org/10.1007/s11760-014-0669-9.

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31

Zeng, Xin, Yunpeng Wu, Shizhe Hu, Ruobin Wang, and Yangdong Ye. "DSPNet: Deep scale purifier network for dense crowd counting." Expert Systems with Applications 141 (March 2020): 112977. http://dx.doi.org/10.1016/j.eswa.2019.112977.

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32

Chaudhry, Huma, Mohd Shafry Mohd Rahim, Tanzila Saba, and Amjad Rehman. "Crowd region detection in outdoor scenes using color spaces." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 02 (March 20, 2018): 1850012. http://dx.doi.org/10.1142/s1793962318500125.

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Анотація:
In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.
33

Xu, Han, Xiangxia Ren, Weiguo Song, Jun Zhang, and Rayyan Saidahmed. "Spatial and temporal analysis of the bottleneck flow under different walking states with a moving obstacle." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 1 (January 1, 2023): 013401. http://dx.doi.org/10.1088/1742-5468/aca2a2.

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Abstract The regulation of a moving obstacle on crowd movement offers the possibility to enhance evacuation efficiency in emergency situations. In this paper, a series of controlled experiments are conducted to study the effect of the moving obstacle on crowd dynamics for pedestrians in three different competitive levels, which respectively correspond to three different walking states. The enhancement effects of the moving obstacle on evacuation efficiency for the crowd in the dual-task and high-motivated walking states are confirmed, and the positions of the moving obstacle are crucial. It is found that the moving obstacle diminishes the order of the trajectories for the crowd in the dual-task and normal walking states, while it boosts near the exit for the crowd in the high-motivated walking state. And the moving obstacle makes the linear backward propagations of stop-and-go wave disappear for the crowd in the dual-task and high-motivated walking states, but the frequency of stop behavior increases for the crowd in the dual-task and normal walking states. The profiles of evacuation time show that the moving obstacle impedes the pedestrian flow from the front of the exit and increases evacuation efficiency for the pedestrians near the walls of the exit. The analysis of time headway suggests that the moving obstacle with a gap of 1.0 m or 1.2 m to the exit can reduce the number of the pedestrians waiting near the exit for the crowd in the dual-task and high-motivated walking states. Besides, the gap of 0.8 m between the moving obstacle and the exit makes the conflicts at the exit is increased, but the gap of 1.0 m or 1.2 m makes the number of conflicts at the exit be reduced. This study helps the evacuation management of dense crowds and improves the design of facilities to facilitate pedestrian traffic.
34

Han, Kang, Wanggen Wan, Haiyan Yao, and Li Hou. "Image Crowd Counting Using Convolutional Neural Network and Markov Random Field." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 4 (July 20, 2017): 632–38. http://dx.doi.org/10.20965/jaciii.2017.p0632.

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In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.
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Khan, Khalil, Rehan Ullah Khan, Waleed Albattah, Durre Nayab, Ali Mustafa Qamar, Shabana Habib, and Muhammad Islam. "Crowd Counting Using End-to-End Semantic Image Segmentation." Electronics 10, no. 11 (May 28, 2021): 1293. http://dx.doi.org/10.3390/electronics10111293.

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Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images. We conducted experiments with four standard crowd-counting datasets, reporting better results as compared to previous results.
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Nuhu, Aliyu Shuaibu, Aamir Saeed, and Ibrahima Faye. "A COMPARATIVE ANALYSIS OF TECHNIQUES FOR CROWD BEHAVIOUR DETECTION IN DENSE SCENES." Platform : A Journal of Science and Technology 4, no. 2 (November 30, 2021): 32. http://dx.doi.org/10.61762/pjstvol4iss2art12757.

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Behaviour analysis is considered a critical area of research in the computer vision research community. Recently, visual monitoring systems of human gathering have found application in different areas such as safety, security, entertainment, and personal archives. Although many approaches have been proposed, certain limitations exist and many unresolved issues remain open. The objective of the paper is to present recent advances on abnormal human behaviour analysis and hierarchical crowd behaviour classification based on the level of complexity. The paper also provides a clear perspective with a broad and in-depth review of the research conducted in this area. In addition, it points out unresolved problems that demand improvement. In general, researchers can use this paper as a starting point for further advancement of behavioural analytic methods to propose novel approaches as well as an exploration of approaches that have received meager attention. This study also investigates the performance comparison of state-of-the-art techniques on anomaly detection: hand-crafted feature approach, and deep learning approach. Finally, limitations of the current methods and promising future research directions are presented.Keywords: Human detection, crowd anomaly, deep learning, crowd analysis, artificial intelligence
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Xiang, Jun, and Na Liu. "Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center." Scientific Programming 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/1990951.

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Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blocks derived from crowd density estimation to train the LR-CNN crowd density estimation model. Finally, experimental results show that the mean absolute error (MAE) and mean square error (MSE) of the proposed method in the UCF_CC_50 dataset are 325.6 and 369.4, respectively. Besides, MAE and MSE in part_A of the Shanghai Tech dataset are 213.5 and 247.1, respectively, and they in part_B are 85.3 and 99.7, respectively. The proposed method effectively improves the accuracy of crowd density estimation in exhibition centers.
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da Silva, Felipe Tavares, Halane Maria Braga Fernandes Brito, and Roberto Leal Pimentel. "Modeling of crowd load in vertical direction using biodynamic model for pedestrians crossing footbridges." Canadian Journal of Civil Engineering 40, no. 12 (December 2013): 1196–204. http://dx.doi.org/10.1139/cjce-2011-0587.

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In the analysis of vibration of footbridges in vertical direction, for crowd situations, there is evidence in the literature that the whole effect of pedestrian action is not well modeled when applying current force-only models to represent such an action. In these models, the action of each pedestrian is represented by a pulsating force applied on the structure. In this paper, a crowd load model is proposed for sparse and dense crowds (with densities up to around 1.0 pedestrian/m2) in which biodynamic models are added to represent the whole action of pedestrians. The focus of the investigation is on vibration effects in vertical direction. Comparisons with measurements on a prototype footbridge were carried out and made it possible to identify differences in the structural response when applying force-only and force-biodynamic models to represent the pedestrian action. The latter (proposed) model resulted in a better agreement with the measurements.
39

Zhu, Aichun, Guoxiu Duan, Xiaomei Zhu, Lu Zhao, Yaoying Huang, Gang Hua, and Hichem Snoussi. "CDADNet: Context-guided dense attentional dilated network for crowd counting." Signal Processing: Image Communication 98 (October 2021): 116379. http://dx.doi.org/10.1016/j.image.2021.116379.

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40

Huang, Liangjun, Luning Zhu, Shihui Shen, Qing Zhang, and Jianwei Zhang. "SRNet: Scale-Aware Representation Learning Network for Dense Crowd Counting." IEEE Access 9 (2021): 136032–44. http://dx.doi.org/10.1109/access.2021.3115963.

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41

Shami, Mamoona Birkhez, Salman Maqbool, Hasan Sajid, Yasar Ayaz, and Sen-Ching Samson Cheung. "People Counting in Dense Crowd Images Using Sparse Head Detections." IEEE Transactions on Circuits and Systems for Video Technology 29, no. 9 (September 2019): 2627–36. http://dx.doi.org/10.1109/tcsvt.2018.2803115.

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42

Hu, Yaocong, Huan Chang, Fudong Nian, Yan Wang, and Teng Li. "Dense crowd counting from still images with convolutional neural networks." Journal of Visual Communication and Image Representation 38 (July 2016): 530–39. http://dx.doi.org/10.1016/j.jvcir.2016.03.021.

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43

钟, 德军. "Dense Crowd Counting Algorithm Based on Dual-Branch Self-Attention." Journal of Image and Signal Processing 13, no. 02 (2024): 130–37. http://dx.doi.org/10.12677/jisp.2024.132012.

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44

Liu, Bangquan, Zhen Liu, Dechao Sun, and Chunyue Bi. "An Evacuation Route Model of Crowd Based on Emotion and Geodesic." Mathematical Problems in Engineering 2018 (October 1, 2018): 1–10. http://dx.doi.org/10.1155/2018/5397071.

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Making unconventional emergent plan for dense crowd is one of the critical issues of evacuation simulations. In order to make the behavior of crowd more believable, we present a real-time evacuation route approach based on emotion and geodesic under the influence of individual emotion and multi-hazard circumstances. The proposed emotion model can reflect the dynamic process of individual in group on three factors: individual emotion, perilous field, and crowd emotion. Specifically, we first convert the evacuation scene to Delaunay triangulation representations. Then, we use the optimization-driven geodesic approach to calculate the best evacuation path with user-specified geometric constraints, such as crowd density, obstacle information, and perilous field. Finally, the Smooth Particle Hydrodynamics method is used for local avoidance of collisions with nearby agents in real-time simulation. Extensive experimental results show that our algorithm is efficient and well suited for real-time simulations of crowd evacuation.
45

Fan, Jiwei, Xiaogang Yang, Ruitao Lu, Xueli Xie, and Weipeng Li. "Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention." Drones 5, no. 3 (July 27, 2021): 68. http://dx.doi.org/10.3390/drones5030068.

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Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.
46

Zhu, Rui, Kangning Yin, Hang Xiong, Hailian Tang, and Guangqiang Yin. "Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning." Wireless Communications and Mobile Computing 2021 (October 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/8586016.

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Wearing masks is an effective and simple method to prevent the spread of the COVID-19 pandemic in public places, such as train stations, classrooms, and streets. It is of positive significance to urge people to wear masks with computer vision technology. However, the existing detection methods are mainly for simple scenes, and facial missing detection is prone to occur in dense crowds with different scales and occlusions. Moreover, the data obtained by surveillance cameras in public places are difficult to be collected for centralized training, due to the privacy of individuals. In order to solve these problems, a cascaded network is proposed: the first level is the Dilation RetinaNet Face Location (DRFL) Network, which contains Enhanced Receptive Field Context (ERFC) module with the dilation convolution, aiming to reduce network parameters and locate faces of different scales. In order to adapt to embedded camera devices, the second level is the SRNet20 network, which is created by Neural Architecture Search (NAS). Due to privacy protection, it is difficult for surveillance video to share in practice, so our SRNet20 network is trained in federated learning. Meanwhile, we have made a masked face dataset containing about 20,000 images. Finally, the experiments highlight that the detection mAP of the face location is 90.6% on the Wider Face dataset, and the classification mAP of the masked face classification is 98.5% on the dataset we made, which means our cascaded network can detect masked faces in dense crowd scenes well.
47

Zhang, Guoli. "Crowd Counting Based on Context-Aware and Multi Scale Feature Fusion." Frontiers in Computing and Intelligent Systems 2, no. 2 (December 26, 2022): 12–15. http://dx.doi.org/10.54097/fcis.v2i2.3736.

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Crowd counting plays an important role in public security. Estimating the number of people in an image with congested crowd accurately is a challenging task. The crowd counting method based on fully convolutional network can perform well in crowd image with complex scene. In this paper, to address the counting problems of occlusion，background clutter and perspective effect, we proposed a simple but effective method called Context-aware Multi scale Fusion Network(CMF Net).The CMF Net applied VGG network as backbone to extract coarse features. Then, three context-aware multi-scale fusion modules (CMFM) are adopted. Each CMFM consist of multi-scale feature extraction module (MEM) and context-aware feature extraction module (CEM). In addition, we propose adaptively dense connection to promoted information transmission in the counting network. Experiments on four datasets demonstrate that our network achieves competitive and effective results.
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Radhan, Ali Raza, Fareed Ahmed Jokhio, Ghulam Hussain, Kamran Javed, and Arsalan Ahmed. "Multi-Scale Pooling In Deep Neural Networks For Dense Crowd Estimation." Sukkur IBA Journal of Emerging Technologies 5, no. 1 (June 30, 2022): 54–63. http://dx.doi.org/10.30537/sjet.v5i1.1023.

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State-of-the-art-methods for counting persons in dense crowded places lack in estimating accurate crowd density due to following reasons. They typically apply the same filters over a complete image or over big image patches. Only then the perspective distortion can be compensated by estimating local scale. It is achieved by training an additional classifier with the optimal kernel size chosen from limited choices. These methods are restricted to the context they are applied on because they are not end-to-end trainable; cannot justify quick scale changes because they allocate a single scale to big image patches; and can only utilize a narrow range of receptive fields for the networks to be of a feasible size. In this study, we bring in an end-to-end trainable deep architecture that merges features achieved from multiple kernels of different sizes and learns various essential features such as quick scale changes and to utilize the right context at each image location. This technique flexibly encodes scale of related information to precisely predict crowd density. The training and validation loss of the proposed approach is 5% and 4% lower than the state-of-the-art context aware method, respectively.
49

Tao, Huiqiang. "Statistical Calculation of Dense Crowd Flow Antiobscuring Method considering Video Continuity." Mathematical Problems in Engineering 2022 (March 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/6185986.

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People flow statistics have important research value in areas such as intelligent security. Accurately identifying the occluded target in video surveillance is a difficulty in the video surveillance system. Now the popular moving object tracking algorithm is based on detection and cannot accurately determine the relationship between overlapping. For the statistics of people flow in the video surveillance system, a dense crowd flow antiocclusion statistical algorithm considering video continuity is proposed. This study focuses on the improved faster R-CN algorithm for small target detection, moving target correlation matching, and two-way human flow intelligent statistics. According to the small-scale characteristics of the human head target, the faster R-CNNV network structure is adaptively improved. The shallow images features are used to improve the feature extraction ability of the network for small targets. The occlusion relationship function is constructed to clearly express the relationship between the occlusion targets, and it is incorporated into the framework of the tracking algorithm. A tracking algorithm based on trajectory prediction is used to follow moving targets in real time, and a two-way human flow intelligent statistical method is used to accomplish human flow. To prove the strength of the method, tests are managed in scenes with different degrees of density, and the results show that the improved target detection algorithm improves the average accuracy of 7.31% and 10.71% on the Brainwash test set and Pets2009 benchmark data set, respectively, compared with the original algorithm. The F-value of the comprehensive evaluation index of video stream of people intelligent statistical method in various scenes can reach more than 90%. Compared with the excellent methods SSD sorting algorithm and yolov3 deepsort algorithm in recent years, its F value is increased by 1.14%–3.04%.
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Sheng, Biyun, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang, and Changyin Sun. "Crowd Counting via Weighted VLAD on a Dense Attribute Feature Map." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 8 (August 2018): 1788–97. http://dx.doi.org/10.1109/tcsvt.2016.2637379.

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