Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

TL;DR

This paper introduces an adaptive compressive tracking method that selects discriminative features online, improving robustness and accuracy in object tracking under appearance variations.

Contribution

It proposes an online vector boosting feature selection, online object representation updating, and trajectory rectification to enhance compressive tracking performance.

Findings

Outperforms state-of-the-art trackers on CVPR2013 benchmark.

Effectively handles large appearance variations.

Improves tracking accuracy and robustness.

Abstract

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects: Firstly, the most discriminative features are selected via an online vector boosting method. Secondly, the object representation is updated in an effective online manner, which preserves the stable features while filtering out the noisy ones. Finally, a simple and effective trajectory rectification approach is adopted that can make the estimated location more accurate. Extensive experiments on the CVPR2013 tracking benchmark demonstrate the superior performance of our algorithm compared over state-of-the-art tracking algorithms.