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
2 MIVIA
3 MIVIA - Machine Intelligence Lab for Video, Image and Audio Processing

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.

Mots clés: video; string kernel; trajectories

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

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. pp.000-0000. ⟨hal-00768648⟩