Title: Activity clustering for anomaly detection

Authors: Xudong Zhu; Hui Li

Addresses: School of Information and Control Engineering, Xi'an University of Architecture and Technology, No. 13, Yanta Road, Xi'an, Shaanxi 710055, China ' School of Telecommunication Engineering, Xidian University, 2 South Taibai Road, Xi'an, Shaanxi 710071, China

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.

Keywords: computer vision; activity clustering; anomaly detection; Bayesian topic models; latent Dirichlet allocation; LDA; surveillance videos; normal activity recognition; activity modelling; unsupervised model selection; hidden Markov models; HMM; likelihood ratio test; security.

DOI: 10.1504/IJIIDS.2013.056389

International Journal of Intelligent Information and Database Systems, 2013 Vol.7 No.5, pp.441 - 453

Received: 13 Feb 2013
Accepted: 22 Apr 2013
Published online: 31 Mar 2014 *

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