Unsupervised group-based crowd dynamic behavior detection and tracking in online video sequences

Analysis of video sequences of public places is an important topic in video surveillance systems. Due to the high probability of occurring abnormal behavior in crowded scene, the main purpose of many surveillance systems is to monitor the crowd movement, and detection of abnormalities. To speed up this process and also for error reduction, it is highly important to use automated and intelligent tools in surveillance systems, as an alternative to the human operator. This study presents an unsupervised and online algorithm for analysis of dynamic crowd behavior, which uses the proposed features, with the capability to analyze crowds over time and reveal different behaviors of the crowd groups. In the proposed algorithm, prominent points are initially tracked. These key points are processed by the proposed system that includes removing the fixed points, employing proposed features of the moving points, automated determination of neighborhood, the similarity of the invariant neighbors. Group clustering is done automatically and the classification stage is conducted without the training phase. The dynamic behavior of the crowd is examined using the features and the extracted group properties and different states in the scene are diagnosed by dynamic thresholding. Experimental evaluation of the proposed method on several databases shows that it is performed properly in video sequences and it is able to detect various abnormal behaviors in the crowd scenes.