An abnormal behavior detection method of video crowds and vehicles based on deep learning

Video monitoring-based exception behavior detection of crowds and vehicles has become a hot research hotspot in image processing, machine vision and other related fields. In view of the difficulty of detecting abnormal targets in complex structured outdoor scenes, an anomaly detection method combining optical flow method and convolutional neural network (CNN) is proposed in this paper, the method can be used to detect and warn abnormal targets in complex structured scenes. Extract video motion characteristics by Lucas-Kanade (LK) optical flow, normalize the extracted optical flow through a simple scaling method, detect and alert the anomalies of video crowds and vehicles adopting CNN, evaluate the abnormal behavior detection method using the accuracy and time. The experiment results show the method can detect the abnormal behaviors of crowds and vehicles in complex scenes in time and effectively.