Online Tracking Parameter Adaptation based on Evaluation

• Main Authors: Chau, Duc Phu, Badie, Julien, Bremond, François, Thonnat, Monique
• Format: Journal Article
• Language: English
• Published: 22-07-2013
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.1307.5653

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.