Crowd counting considering network flow constraints in videos

Gao, Liqing; Wang, Yanzhang; Ye, Xin; Wang, Jian

doi:10.1049/iet-ipr.2016.0994

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…e effect is improved, but the robustness of human detection is still not high. Crowd density analysis and crowd counting based on regression are mainly used to learn the mapping relationship between image features and number of people [25]. Image segmentation is based on the regression method first, the image, texture, edge, and the prospect of gradient low-level features such as extraction and then the linear regression, Gaussian regression, ridge regression, and regression function are studied, such as learning exists in the mapping function of the number of low-level features and the image, generating a static background model, which is sensitive to illumination changes.…”

Section: Related Workmentioning

confidence: 99%

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

Chen

Dinavahi

2021

Scientific Programming

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With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex scene make the analysis of group behavior in video face great challenges. Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition in the video. A crowd detection and location method based on density map regression-guided classification was designed. Finally, a crowd behavior analysis method based on density grade division was designed to complete crowd density analysis and video group behavior detection. In addition, this paper also proposes to extract spatio-temporal features of crowd posture and density by using the double-flow spatio-temporal map network model, so as to effectively capture the differentiated movement information among different groups. Experimental results on public datasets show that the proposed method has high accuracy and can effectively predict group behavior.

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Section: Related Workmentioning

confidence: 99%

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

Chen

Dinavahi

2021

Scientific Programming

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“…Recently, more and more works have been proposed by the researcher to tackle the crowd counting task in computer vision [2,3,[5][6][7][8][9][10][11][12][13]. Earlier works addressed crowd counting as an object detection problem.…”

Section: Related Workmentioning

confidence: 99%

Feature channel enhancement for crowd counting

Kong²,

Zheng³

et al. 2020

IET image process

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Crowd counting, i.e. count the number of people in a crowded visual space, is emerging as an essential research problem with public security. A key in the design of the crowd counting system is to create a stable and accurate robust model, which requires to process on the feature channels of the counting network. In this study, the authors present a featured channel enhancement (FCE) block for crowd counting. First, they use a feature extraction unit to obtain the information of each channel and encodes the information of each channel. Then use a non‐linear variation unit to deal with the encoded channel information, finally, normalise the data and affixed to each channel separately. With the use of the FCE, the positive characteristic channel can be enhanced and weak or negative channel information can be suppressed. The authors successfully incorporate the FCE with two compact networks on the standard benchmarks and prove that the proposed FCE achieves promising results.

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“…With the continuous growth of the world population and the increase of human diversi ed social activities, the situation of large population gatherings often appears in our daily life. Accurate counting and density prediction of dense crowds have been widely used in the elds of public safety management [7], urban planning [8], and video surveillance [9]. Moreover, the statistical method of dense crowds can also be extended to similar statistical work, such as cell statistics in medical research, vehicle statistics in tra c jams, and extended sample surveys in biology.…”

Section: Introductionmentioning

confidence: 99%

SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

Xiong

Huang

et al. 2022

Mathematical Problems in Engineering

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Accurate counting in dense scenes can effectively prevent the occurrence of abnormal events, which is crucial for flow management, traffic control, and urban safety. In recent years, the application of deep learning technology in counting tasks has significantly improved the performance of models, but it still faces many challenges, including the diversity of target distribution between image and background, the drastic change of target scale, and serious occlusion. To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality estimated density maps. SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then calibrates and refuses the fused feature maps through a channel spatial attention-aware module, which improves the model’s ability to suppress background and focus on main features. Finally, the final estimated density map is generated by a dilated convolution module. We conduct experiments on five public crowd datasets, UCF_CC_50, WorldExpo’10, ShanghaiTech, Mall, and Beijing BRT, and the results show that our method achieves lower counting errors than existing state-of-the-art methods. In addition, we extend SCFFNet to count other objects, such as vehicles in the vehicle dataset HBR_YD, and the experimental results show that our proposed method significantly improves the output quality with higher accuracy than previous methods.

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Crowd counting considering network flow constraints in videos

Cited by 8 publications

References 26 publications

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

Feature channel enhancement for crowd counting

SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

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