Activity clustering for anomaly detection

Abstract

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 modelling and anomaly detection without any manual labelling 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 spatio-temporal actions, where we analyse 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, named latent Dirichlet Markov clustering LDMC. The approach builds on hidden Markov models HMMs and latent Dirichlet allocation LDA, and overcomes their drawbacks on accuracy, robustness and computational efficiency. 3 A runtime accumulative anomaly measure is introduced to detect abnormal activity, whereas normal activities are recognised 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 real surveillance scenarios.