A clustering algorithm of trajectories for behaviour understanding based on string kernels

Luc Brun 1 A. Saggese 2 Mario Vento 3
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
2 MIVIA
MIVIA - Machine Intelligence Lab for Video, Image and Audio Processing
Abstract : This work aims to identify abnormal behaviors from the analysis of humans or vehicles' trajectories. A set of normal trajectories' prototypes is extracted by means of a novel unsupervised learning technique: the scene is adaptively partitioned into zones by using the distribution of the training set and each trajectory is represented as a sequence of symbols by taking into account positional information (the zones crossed in the scene), speed and shape. The main novelties of this work are the following: first, the similarity between trajectories is evaluated by means of a kernel-based approach. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. The proposed approach has been compared with state-of-the-art methods and it clearly outperforms all the other considered techniques.
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Communication dans un congrès
The 8th International Conference on Signal Image Technology & Internet Based Systems, Nov 2012, Naples, Italy. IEEE Computer Society, pp.000-0000, 2012
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Luc Brun, A. Saggese, Mario Vento. A clustering algorithm of trajectories for behaviour understanding based on string kernels. The 8th International Conference on Signal Image Technology & Internet Based Systems, Nov 2012, Naples, Italy. IEEE Computer Society, pp.000-0000, 2012. 〈hal-00768648〉

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