Online Tracking Parameter Adaptation based on Evaluation

Abstract : Parameter tuning is a common issue for many tracking algorithms. In order to solve this problem, this paper proposes an online parameter tuning to adapt a tracking algorithm to various scene contexts. In an offline training phase, this approach learns how to tune the tracker parameters to cope with different contexts. In the online control phase, once the tracking quality is evaluated as not good enough, the proposed approach computes the current context and tunes the tracking parameters using the learned values. The experimental results show that the proposed approach improves the performance of the tracking algorithm and outperforms recent state of the art trackers. This paper brings two contributions: (1) an online tracking evaluation, and (2) a method to adapt online tracking parameters to scene contexts.
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https://hal.inria.fr/hal-00846920
Contributor : Duc Phu Chau <>
Submitted on : Monday, July 22, 2013 - 11:08:48 AM
Last modification on : Tuesday, July 24, 2018 - 3:48:04 PM
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  • HAL Id : hal-00846920, version 1
  • ARXIV : 1307.5653

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Duc Phu Chau, Julien Badie, François Bremond, Monique Thonnat. Online Tracking Parameter Adaptation based on Evaluation. IEEE International Conference on Advanced Video and Signal-based Surveillance, Aug 2013, Krakow, Poland. ⟨hal-00846920⟩

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