Online Tracking Parameter Adaptation based on Evaluation
This addresses the issue of manual parameter tuning for tracking algorithms in computer vision, though it appears incremental as it builds on existing tracking methods.
The paper tackles the problem of parameter tuning for tracking algorithms by proposing an online parameter adaptation method that adjusts tracker parameters based on scene contexts, improving performance and outperforming recent state-of-the-art trackers.
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.