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

Xinfang Chen,Venkata Dinavahi,Shah Nazir

doi:10.1155/2021/2934943

Xinfang Chen, Venkata Dinavahi + Show 1 more

Open Access

https://doi.org/10.1155/2021/2934943

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Abstract

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.

Highlights

E processing of video images through computer vision can further replace manual monitoring to perform real-time and efficient monitoring of crowd density and crowd behavior
E main innovations and contributing points of this paper is to propose a group behavior pattern recognition algorithm [21, 22] based on spatio-temporal graph convolutional network, which can effectively recognize group behavior. e paper proposed to use the dual-stream spatio-temporal map network model to extract spatiotemporal features of the crowd posture and density to effectively capture the differentiated movement information between different crowds
We take group density analysis and group behavior recognition in video as the goal and propose a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network

Summary

Introduction

E processing of video images through computer vision can further replace manual monitoring to perform real-time and efficient monitoring of crowd density and crowd behavior. E paper proposed to use the dual-stream spatio-temporal map network model to extract spatiotemporal features of the crowd posture and density to effectively capture the differentiated movement information between different crowds. Sam et al [28] proposed a switching network based on MCNN, which has multiple CNN subnetworks with different depths and different convolution kernel sizes of each subnetwork, improving the accuracy and robustness of crowd density analysis and crowd count results of high occlusion and multiscale scene transformation.

Results

Conclusion