Real-Time Detection and Simulation of Abnormal Crowd Behavior

Aguilar, Wilbert G.; Luna, Marco A.; Moya, Julio F.; Luna, Marco P.; Abad, Vanessa; Ruiz, Hugo; Parra, Humberto

doi:10.1007/978-3-319-60928-7_36

Cited by 11 publications

(1 citation statement)

References 23 publications

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“…In the detection of crowd density [17,18] and crowd behavior [19] by a computer, the movement of the crowd is complex and the scene is changeable. Due to the change of illumination, the blocking of the crowd, the perspective effect, the different shooting angles, and other factors, it will bring difficulties to the detection by the computer.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%