Crowd Abnormal Behavior Detection Based on Machine Learning

The detection of abnormal behavior is an important area of research in computer vision and is also driven by a wide of application domains, such as intelligent video surveillance. However, there are few detection algorithms to recognize abnormal behavior in crowds. This study proposed a novel method which can detect whether the crowd is abnormal or not in particular scene, such as stampede, fight and panic. For this purpose, a kind of feature extraction and description scheme has been put forward for particle flow information about crowd motion applying to space-time features cubes. The detection algorithm combined with space-time feature cubes and competitive neural network model is proposed to detect abnormal events in global region. The experimental results show that our approach achieves superior performance to abnormal behavior detection in crowds.