Robust Structured Group Local Sparse Tracker Using Deep Features

TL;DR

This paper introduces a deep features-based structured group local sparse tracker that leverages group sparsity and local patch information within a particle filter framework, demonstrating superior tracking performance on benchmark datasets.

Contribution

It proposes a novel optimization model with group-sparsity regularization for robust visual tracking using deep local patch features.

Findings

Outperforms several state-of-the-art trackers on OTB50 and OTB100 benchmarks.

Employs an efficient algorithm with closed-form solutions for real-time tracking.

Shows robustness to challenging tracking scenarios.

Abstract

Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking result. In this paper, we propose a robust deep features-based structured group local sparse tracker (DF-SGLST), which exploits the deep features of local patches inside target candidates and represents them by a set of templates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in DF-SGLST employs a group-sparsity regularization term to seamlessly adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the closed-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.