BackTrack: Robust template update via Backward Tracking of candidate template

TL;DR

BackTrack introduces a backward tracking method to assess candidate template quality, enabling reliable template updates during visual object tracking and improving robustness against appearance variations.

Contribution

It presents a novel backward tracking approach to quantify template confidence, enhancing template update reliability in various trackers.

Findings

Achieves state-of-the-art performance on multiple tracking benchmarks.

Effectively handles appearance variations like deformation and illumination.

Reduces model drift by reliable template updating.

Abstract

Variations of target appearance such as deformations, illumination variance, occlusion, etc., are the major challenges of visual object tracking that negatively impact the performance of a tracker. An effective method to tackle these challenges is template update, which updates the template to reflect the change of appearance in the target object during tracking. However, with template updates, inadequate quality of new templates or inappropriate timing of updates may induce a model drift problem, which severely degrades the tracking performance. Here, we propose BackTrack, a robust and reliable method to quantify the confidence of the candidate template by backward tracking it on the past frames. Based on the confidence score of candidates from BackTrack, we can update the template with a reliable candidate at the right time while rejecting unreliable candidates. BackTrack is a generic template update scheme and is applicable to any template-based trackers. Extensive experiments on various tracking benchmarks verify the effectiveness of BackTrack over existing template update algorithms, as it achieves SOTA performance on various tracking benchmarks.