Robust compressive tracking via online weighted multiple instance learning

TL;DR

This paper introduces a robust object tracking method combining sparse representation, a coarse-to-fine search strategy, and weighted multiple instance learning to effectively handle occlusion, motion blur, and other challenges.

Contribution

It presents a novel tracking algorithm that integrates sparse representation with weighted multiple instance learning and a coarse-to-fine search, improving robustness and accuracy.

Findings

Outperforms existing trackers on benchmark datasets.

Effectively handles occlusion, deformation, and background clutter.

Achieves high accuracy with reduced complexity.

Abstract

Developing a robust object tracker is a challenging task due to factors such as occlusion, motion blur, fast motion, illumination variations, rotation, background clutter, low resolution and deformation across the frames. In the literature, lots of good approaches based on sparse representation have already been presented to tackle the above problems. However, most of the algorithms do not focus on the learning of sparse representation. They only consider the modeling of target appearance and therefore drift away from the target with the imprecise training samples. By considering all the above factors in mind, we have proposed a visual object tracking algorithm by integrating a coarse-to-fine search strategy based on sparse representation and the weighted multiple instance learning (WMIL) algorithm. Compared with the other trackers, our approach has more information of the original signal with less complexity due to the coarse-to-fine search method, and also has weights for important samples. Thus, it can easily discriminate the background features from the foreground. Furthermore, we have also selected the samples from the un-occluded sub-regions to efficiently develop the strong classifier. As a consequence, a stable and robust object tracker is achieved to tackle all the aforementioned problems. Experimental results with quantitative as well as qualitative analysis on challenging benchmark datasets show the accuracy and efficiency of our method.