Automatic Parameter Adaptation for Multi-object Tracking

Duc Phu Chau 1 Monique Thonnat 1 François Bremond 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Object tracking quality usually depends on video context (e.g. object occlusion level, object density). In order to decrease this dependency, this paper presents a learning approach to adapt the tracker parameters to the context variations. In an offline phase, satisfactory tracking parameters are learned for video context clusters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The experimental results show that the proposed approach outperforms the recent trackers in state of the art. This paper brings two contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.
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Communication dans un congrès
International Conference on Computer Vision Systems (ICVS), Jul 2013, St Petersburg, Russia. Springer, 2013, Lecture Notes in Computer Science
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Soumis le : samedi 11 mai 2013 - 16:14:07
Dernière modification le : mardi 24 juillet 2018 - 15:48:06
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  • HAL Id : hal-00821669, version 1
  • ARXIV : 1305.2687

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Duc Phu Chau, Monique Thonnat, François Bremond. Automatic Parameter Adaptation for Multi-object Tracking. International Conference on Computer Vision Systems (ICVS), Jul 2013, St Petersburg, Russia. Springer, 2013, Lecture Notes in Computer Science. 〈hal-00821669〉

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