Particle Filter-Based Prediction for Anomaly Detection in Automatic Surveillance

Automatic surveillance of abnormal events is a major unsolved problem in city management. By successful implementation of automatic surveillance of abnormal events, a significant amount of human resources in video monitoring can be economized. One solution to this application is computer vision technology. This approach utilizes an image processing algorithm to extract specific features and then uses discriminator algorithms to give an alert. In this paper, we propose to apply a particle filter-based algorithm to feature series extracted from videos in order to give alerts when abnormal events occur. The whole process consists of feature series generation and particle filter tracking. To represent the features of a video, an L2-norm extractor is designed based on the optical flow. Then, the particle filter keeps track of these feature series. The occurrence of abnormal events will cause the shift of feature series and a large error in PF tracking. This, in turn, will allow computers to understand and define the occurrences of anomalies. Experiments on UMN dataset show that our algorithm reaches 90% accuracy in frame-level detection.