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Dynamic and Robust Object Tracking for Activity Recognition

Duc Phu Chau 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This thesis presents a new control approach for mobile object tracking. More precisely in order to cope with the tracking context variations, this approach learns how to tune the parameters of tracking algorithms based on object appearance or points of interest. The tracking context of a video sequence is defined as a set of features: density of mobile objects, their occlusion level, their contrasts with regard to the background and their 2D areas. Each contextual feature is represented by a code-book model. In an offline supervised learning phase, satisfactory tracking parameters are searched for each training video sequence. Then these video sequences are classified by clustering their contextual features. Each context cluster is associated with the learned tracking parameters. In the online control phase, two approaches are proposed. In the first one, once a context change is detected, the tracking parameters are tuned using the learned values. In the second approach, the parameter tuning is performed when the context changes and the tracking quality (computed by an online evaluation algorithm) is not good enough. An online learning process enables to update the context/parameter relations. The approach has been experimented on long, complex videos and some public video datasets. This thesis proposes five contributions: (1) a classification method of video sequences to learn offline the tracking parameters, (2) an online tracking evaluation algorithm, (3) a method to tune and learn online the tracking parameters, (4) a tunable object descriptor-based tracking algorithm enabling adaptation to scene conditions, (5) a robust mobile object tracker based on Kalman filter and global tracking.
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Contributor : Duc Phu Chau <>
Submitted on : Thursday, November 22, 2012 - 3:53:29 PM
Last modification on : Thursday, March 5, 2020 - 5:34:21 PM
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  • HAL Id : tel-00695567, version 1



Duc Phu Chau. Dynamic and Robust Object Tracking for Activity Recognition. Computer Vision and Pattern Recognition [cs.CV]. Institut National de Recherche en Informatique et en Automatique (INRIA), 2012. English. ⟨tel-00695567⟩



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