Detection of abnormal behaviors using a mixture of Von Mises distributions

Authors:
Simone Calderara;Rita Cucchiara;Andrea Prati
Affiliations:
D.I.I. University of Modena and Reggio Emilia, Via Vignolese, 905 - 41100 - Italy;D.I.I. University of Modena and Reggio Emilia, Via Vignolese, 905 - 41100 - Italy;DI.S.M.I. University of Modena and Reggio Emilia, Via Amendola, 2 - 42100 - Italy
Venue:
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Year:
2007

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Abstract

This paper proposes the use of a mixture of Von Mises distributions to detect abnormal behaviors of moving people. The mixture is created from an unsupervised training set by exploiting k-medoids clustering algorithm based on Bhattacharyya distance between distributions. The extracted medoids are used as modes in the multi-modal mixture whose weights are the priors of the specific medoid. Given the mixture model a new trajectory is verified on the model by considering each direction composing it as independent. Experiments over a real scenario composed of multiple, partially-overlapped cameras are reported.