Part-based Visual Tracking via Structural Support Correlation Filter

TL;DR

This paper introduces a novel part-based structural support correlation filter tracker that improves real-time performance and robustness against partial occlusion by jointly learning support filters with a star structure model and incorporating inter-frame consistency.

Contribution

The paper proposes a new part-based tracking method combining support correlation filters with a star structure model and inter-frame consistency for improved accuracy and efficiency.

Findings

Outperforms state-of-the-art trackers on benchmark datasets.

Achieves higher tracking accuracy, speed, and robustness.

Effectively handles partial occlusion and scale changes.

Abstract

Recently, part-based and support vector machines (SVM) based trackers have shown favorable performance. Nonetheless, the time-consuming online training and updating process limit their real-time applications. In order to better deal with the partial occlusion issue and improve their efficiency, we propose a novel part-based structural support correlation filter tracking method, which absorbs the strong discriminative ability from SVM and the excellent property of part-based tracking methods which is less sensitive to partial occlusion. Then, our proposed model can learn the support correlation filter of each part jointly by a star structure model, which preserves the spatial layout structure among parts and tolerates outliers of parts. In addition, to mitigate the issue of drift away from object further, we introduce inter-frame consistencies of local parts into our model. Finally, in our model, we accurately estimate the scale changes of object by the relative distance change among reliable parts. The extensive empirical evaluations on three benchmark datasets: OTB2015, TempleColor128 and VOT2015 demonstrate that the proposed method performs superiorly against several state-of-the-art trackers in terms of tracking accuracy, speed and robustness.