A Self-Trained Spatial Graph Convolutional Network for Unsupervised Human-Related Anomalous Event Detection in Complex Scenes

Most of previous works on abnormal event detection take this task as a novelty detection problem, which employ the supervised setting that needs videos containing only normal events for learning normal patterns. However, few works are developed under the unsupervised setting that detects anomaly without labeled normal videos. In this article, we develop a novel unsupervised algorithm using the skeleton feature for detecting human-related anomalous events. Our method applies the idea of self-training regression for iteratively updating the anomaly scores of skeletons for anomaly detection. In detail, each extracted skeleton is first decomposed into global and local feature components. Then, an unsupervised anomaly detector is operated on these two components to generate the initial anomalous and normal skeleton sets. These two sets are utilized to optimize parameters of an anomaly scoring module consisting of a spatial graph convolutional network (SGCN) and fully connected layers. The trained module is then employed to recalculate anomaly scores of all skeletons to update memberships of pseudo anomalous and normal skeletons set for the next training procedure, and this process is performed in an iterative way to get superior anomaly detection performance. Experimental results on two challenging data sets and their subsets that only contain human-related anomalies demonstrate our method outperforms several state-of-the-art supervised methods.