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Using circular statistics for trajectory shape analysis

Abstract: The analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM (Expectation Maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data. ©2008 IEEE.


Prati, Andrea; Calderara, Simone; Cucchiara, Rita "Using circular statistics for trajectory shape analysis" 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 23-28 June 2008, Anchorage (AK), pp. 3847 -3854 , 2008, 2008 DOI: 10.1109/CVPR.2008.4587837

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