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Human behavior clustering for anomaly detection

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Abstract

This paper aims to address the problem of modeling human behavior patterns captured in surveillance videos for the application of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling and online anomaly detection without the need for manual labeling of the training data set. The framework consists of the following key components. 1) A compact and effective behavior representation method is developed based on spatial-temporal interest point detection. 2) The natural grouping of behavior patterns is determined through a novel clustering algorithm, topic hidden Markov model (THMM) built upon the existing hidden Markov model (HMM) and latent Dirichlet allocation (LDA), which overcomes the current limitations in accuracy, robustness, and computational efficiency. The new model is a four-level hierarchical Bayesian model, in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern is a distribution over some segments of the video. Each of these segments in the video can be modeled as a mixture of actions where each action is a distribution over spatial-temporal words. 3) An online anomaly measure is introduced to detect abnormal behavior, whereas normal behavior is recognized by runtime accumulative visual evidence using the likelihood ratio test (LRT) method. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Xudong Zhu & Zhijing Liu

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  1. Xudong Zhu
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  2. Zhijing Liu
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Correspondence to Xudong Zhu.

Additional information

Xudong Zhu is a PhD candidate at School of Computer Science and Technology, Xidian University. He received his B S degree from Xidian University in 1996, the M.Sc degree in computer science from Northwestern Polytechnic University in 2005. His research interest is data mining.

Zhijing Liu is a Professor and advisor for doctoral students at School of Computer Science and Technology, Xidian University. He received his bachelor degree from Xidian University in 1982. His research works focus on the fields of vision computing technologies, network multimedia technologies, technologies of virtual reality, and key technologies of Egovernment and E-commerce.

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Zhu, X., Liu, Z. Human behavior clustering for anomaly detection. Front. Comput. Sci. China 5, 279–289 (2011). https://doi.org/10.1007/s11704-011-0080-4

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  • DOI: https://doi.org/10.1007/s11704-011-0080-4

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