Patchwise object tracking via structural local sparse appearance model

TL;DR

This paper introduces a robust visual tracking method that leverages patchwise joint sparse representation and multi-scale local features to improve target tracking accuracy, especially under occlusion and appearance changes.

Contribution

It presents a novel patchwise joint sparse appearance model that considers local features at multiple scales and target relationships over time for enhanced tracking performance.

Findings

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

Effectively handles occlusion and appearance variations

Utilizes multi-scale local features for robust tracking

Abstract

In this paper, we propose a robust visual tracking method which exploits the relationships of targets in adjacent frames using patchwise joint sparse representation. Two sets of overlapping patches with different sizes are extracted from target candidates to construct two dictionaries with consideration of joint sparse representation. By applying this representation into structural sparse appearance model, we can take two-fold advantages. First, the correlation of target patches over time is considered. Second, using this local appearance model with different patch sizes takes into account local features of target thoroughly. Furthermore, the position of candidate patches and their occlusion levels are utilized simultaneously to obtain the final likelihood of target candidates. Evaluations on recent challenging benchmark show that our tracking method outperforms the state-of-the-art trackers.