Activity clustering for online anomaly detection

This paper aims to address the problem of clustering activities captured in surveillance videos for the applications of online normal activity recognition and anomaly detection. A novel framework is developed for automatic activity 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 activity representation method as a stochastic sequence of spatiotemporal actions, where we analyze the global structural information of activities using their local action statistics. 2) The natural grouping of activities is discovered through a novel clustering algorithm with unsupervised model selection. 3) A runtime accumulative anomaly measure is introduced to detect abnormal activities, whereas normal activities 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 activity 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.