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Analyse de trajectoires vidéos à l’aide de modélisations markoviennes pour l’interprétation de contenus
2009 March, 05
254 pages
Language: French
Team(s): VISTA
Keywords: Video trajectories, recognition of events and shapes, detection of unexpected events, recognition of activities, sport video analysis
Summary:
This thesis deals with the analysis of dynamic contents in videos. Our approach relies on trajectories extracted from the processed image sequences. The developed method is invariant to translation, rotation and scaling while taking into account both shape and dynamics-related information on the trajectories. A novel hidden Markov model (HMM) framework is proposed which is able in particular to handle small sets of observations. Parameter setting is properly adressed. A similarity measure between the HMM is defined and exploited to tackle three dynamic video content understanding tasks : supervised recognition, clustering and detection of unexpected events. We have conducted experiments on several significative sets of real videos including sport videos. Then, hierarchical semi-Markov chains are introduced to process trajectories of several interacting moving objects. The temporal interactions between trajectories are taken into account and exploited to characterize relevant phases of the activities in the processed videos. Our method has been favorably evaluated on sets of trajectories extracted from squash and handball videos. Applications of such interaction-based models have also been extended to 3D gesture and action recognition and clustering, and temporal segmentation of actions. The results show that taking into account the interactions is of great interest for such applications.