Activity clustering for anomaly detection. (1st January 2013)

• Record Type: Journal Article
• Authors: Zhu, Xudong; Li, Hui
• Is Part Of: International journal of intelligent information and database systems. Volume 7:Number 5(2013)
• Page Start: 441
• Page End: 453
• Publication Date: 2013-01-01
• Subjects: computer vision -- clustering -- anomaly detection -- Bayesian topic models -- latent Dirichlet allocation -- LDA; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.