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.
Detection of Abnormal Behaviors using a Mixture of Von Mises Distributions / Calderara, Simone; Cucchiara, Rita; Prati, Andrea. - ELETTRONICO. - (2007), pp. 141-146. (Intervento presentato al convegno IEEE Conference on Advanced Video and Signal based Surveillance tenutosi a London (UK) nel 5-7 September 2007) [10.1109/AVSS.2007.4425300].
Detection of Abnormal Behaviors using a Mixture of Von Mises Distributions
CALDERARA, Simone;CUCCHIARA, Rita;PRATI, Andrea
2007
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.File | Dimensione | Formato | |
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