# Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise

## TL;DR

This paper introduces a robust visual tracking method that uses dynamic classifier selection with sparse label noise representation, effectively handling appearance changes and label noise to improve tracking accuracy in challenging scenarios.

## Contribution

It proposes a novel classifier selection approach that models label noise with sparse representation and integrates it into a part-based online boosting framework for enhanced tracking.

## Key findings

- Outperforms state-of-the-art trackers on challenging sequences
- Effectively handles occlusions, illumination changes, and pose variations
- Demonstrates robustness and accuracy in qualitative and quantitative evaluations

## Abstract

Recently a category of tracking methods based on "tracking-by-detection" is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.