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.

中文翻译：

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基于时空图卷积网络的群体行为模式识别算法

随着人口的快速增长，人群活动更加多样化，以及社会化进程的快速发展，群体场景变得越来越普遍，因此对视频中群体行为数据的建模、分析和理解的需求也越来越大。与以往的视频内容分析工作相比，群体视频中的人数不断增加、场景更加复杂等因素，使得视频中群体行为的分析面临着巨大的挑战。因此，本文针对视频中的群体密度分析和群体行为识别，提出了一种基于时空图卷积网络的群体行为模式识别算法。设计了一种基于密度图回归引导分类的人群检测定位方法。最后，设计了一种基于密度等级划分的人群行为分析方法，完成人群密度分析和视频群体行为检测。此外，本文还提出利用双流时空地图网络模型提取人群姿态和密度的时空特征，从而有效捕捉不同群体之间的差异化运动信息。在公共数据集上的实验结果表明，该方法具有较高的准确率，可以有效地预测群体行为。从而有效捕捉不同群体之间差异化的运动信息。在公共数据集上的实验结果表明，该方法具有较高的准确率，可以有效地预测群体行为。从而有效捕捉不同群体之间差异化的运动信息。在公共数据集上的实验结果表明，该方法具有较高的准确率，可以有效地预测群体行为。