Online Tracking Parameter Adaptation based on Evaluation

TL;DR

This paper introduces an online parameter adaptation method for tracking algorithms that improves performance by dynamically tuning parameters based on scene context evaluation, combining offline learning and online adjustment.

Contribution

It presents a novel online tracking evaluation method and a dynamic parameter tuning approach that adapts to various scene contexts in real-time.

Findings

Improved tracking performance over baseline methods.

Outperforms recent state-of-the-art trackers.

Effective adaptation to diverse scene contexts.

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.