Behavior Clustering for Anomaly Detection

Abstract

This paper aims to address the problem of clustering behaviors captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and anomaly detection without any manual labeling of the training data set. The framework consists of the following key components: 1) Drawing from natural language processing, we introduce a compact and effective behavior representation method as a stochastic sequence of spatiotemporal events, where we analyze the global structural information of behaviors using their local action statistics. 2) The natural grouping of behaviors is discovered through a novel clustering algorithm with unsupervised model selection. 3) A run-time accumulative anomaly measure is introduced to detect abnormal behaviors, whereas normal behaviors are recognized when sufficient visual evidence has become available based on an online Likelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normal behavior recognition at the shortest possible time. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.