Multiple Instance Relational Learning for Video Anomaly Detection

Most existing video anomaly detection methods are dependent on strong supervision to achieve satisfactory performance, which could be laborious and impractical. Besides, methods using weakly supervised learning less consider the relations among event proposals. To this end, we propose an anomaly event detection method by the instance-based event proposal generation and the proposal relation learning. Specifically, the event proposals are generated by sampling temporal distributions from the multiple instance learning (MIL), while relations among the proposals are captured by graph convolutional network for anomaly localization and classification. The proposed method is free from strong frame-level supervision but only requires video-level annotations. We conduct experiments on four datasets, i.e., UCF crime, UCSD-Peds, UMN, and CityScene, and show state-of-the-art performance on anomaly recognition and detection tasks.