Crowd Gathering Detection Based on the Foreground Stillness Model

Abstract

The abnormal crowd behavior detection is an important research topic in computer vision to improve the response time of critical events. In this letter, we introduce a novel method to detect and localize the crowd gathering in surveillance videos. The proposed foreground stillness model is based on the foreground object mask and the dense optical flow to measure the instantaneous crowd stillness level. Further, we obtain the long-term crowd stillness level by the leaky bucket model, and the crowd gathering behavior can be detected by the threshold analysis. Experimental results indicate that our proposed approach can detect and locate crowd gathering events, and it is capable of distinguishing between standing and walking crowd. The experiments in realistic scenes with 88.65% accuracy for detection of gathering frames show that our method is effective for crowd gathering behavior detection.