基于因果分析的群体行为识别

Group Activity Recognition in Crowd via Causality Analysis

该文提出一种基于因果分析的群体行为识别方法,利用Grange因果检验分析个体行为间的因果关系,在此基础上,结合个体间的因果关系、空间位置关系和视觉注意力范围,利用基于主集的聚类法检测行为群体。为了有效地表示群体行为,用方向梯度直方图和光流直方图描述个体行为,用因果特征描述个体间的交互行为。采用稀疏表示进行群体行为识别,在公共数据库BEHAVE和collective activity上对该方法进行验证,并与其他方法进行对比试验,结果表明了该方法的有效性。

A novel method for group activity recognition in crowd is proposed using causality analysis. TheGranger Causality Test is used to analyze the causality between individual actions. On this basis, we adopt adominant set based clustering algorithm to detect interacting groups in crowded scenes using causality, spatial anddirectional relationships among people. To effectively represent group activity, low level visual features andcausality features are used. The low level visual features, which included histograms of oriented gradients （HOG）and histograms of optical flow （HOF）, are applied to describe the properties of individual activity, and the causalityfeatures obtained by causality analysis are introduced to depict the interaction information of people. Sparserepresentation is employed to recognize group activities in crowd. Experiments are performed on the BEHAVE andcollective activity databases to test and evaluate the proposed method. The experiments results show that theproposed method is more effective than other state-of-the-art methods.