# Structured Group Local Sparse Tracker

## TL;DR

The paper introduces a structured group local sparse tracker (SGLST) that leverages local patches and spatial layout information within a particle filter framework, demonstrating superior tracking performance.

## Contribution

It proposes a novel optimization model with group-sparsity regularization to incorporate spatial structure in local sparse tracking.

## Key findings

- Outperforms several state-of-the-art trackers on challenging benchmarks.
- Efficient numerical algorithm with closed-form solutions for optimization.
- Effective utilization of local patches and spatial layout improves tracking accuracy.

## Abstract

Sparse representation is considered as a viable solution to visual tracking. In this paper, we propose a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimization model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularization term. To solve the optimization model, we propose an efficient numerical algorithm consisting of two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmarks of challenging image sequences demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.