Robust Object Tracking Based on Self-adaptive Search Area

TL;DR

This paper introduces a self-adaptive search area strategy for DCF-based object trackers, improving robustness against boundary effects during fast motion and enhancing real-time performance.

Contribution

It proposes a novel adaptive search area method that adjusts based on target motion, reducing boundary effects and improving tracking accuracy and efficiency.

Findings

Improved tracking accuracy on OTB benchmark.

Enhanced robustness during fast motion and motion blur.

Achieved real-time performance with adaptive search.

Abstract

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating the object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.