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Spatial-Temporal Words Learning for Crowd Behavior Recognition

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2012

Key Words:

Author Name
Abstract (2. Language): 
In this paper we introduce a method to detect abnormal behavior in crowd scenes using spatial-temporal words, in which Spatial Temporal Interest Points (STIPs) are extracted as crowd behavior movement features. For this purpose, three methods for STIPs extraction are compared and analyzed, which are Harris corner, Gabor wavelet and Hessian Matrix. Then we select Hessian matrix which can get scale-invariant STIPs. Gradient histogram, optical flow histogram and spatio-temporal Haar feature are used to build descriptors for STIPs. In normal behavior modeling Bag-of-words strategy is used, the keywords of which are produced by GMM based on EM estimation. Then training samples are divided into several clips which are described in probability vectors using keywords, we combine all vectors as a normal behavior codebook. In recognition phase, we calculate the similarity distance between the coding vector of the test samples and the codebook, the abnormal behavior can be detected when the minimal distance exceeds a threshold. We verify the algorithm in UMN and UCF datasets, which shows that the proposed algorithm has effective identification for crowd abnormal behavior, and it has good robustness against scale variant and illumination changing
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