Improving Model Drift for Robust Object Tracking

TL;DR

This paper introduces a robust object tracking method that detects primary and secondary peaks in response maps, employs an adaptive confidence mechanism, and uses multi-feature merging to improve model drift handling in complex scenes, achieving real-time performance.

Contribution

It presents a novel correlation filter-based tracker with adaptive update and multi-feature merging, enhancing robustness against model drift in complex environments.

Findings

Outperforms several state-of-the-art trackers on OTB datasets

Operates in real time with improved robustness

Effectively handles complex scene variations

Abstract

Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers' multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs at speed in real time.