Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection

TL;DR

This paper introduces a robust regression-based visual tracking method using iterative gradient descent and threshold selection, improving accuracy and robustness against outliers in challenging video sequences.

Contribution

It proposes a novel robust linear regression estimator and an iterative algorithm (IGDTS) for outlier handling, extending to a generative tracker with an update scheme for appearance changes.

Findings

Outperforms existing trackers on challenging sequences

Effective handling of outliers with IGDTS algorithm

Improved target estimation accuracy

Abstract

Visual tracking fundamentally involves regressing the state of the target in each frame of a video. Despite significant progress, existing regression-based trackers still tend to experience failures and inaccuracies. To enhance the precision of target estimation, this paper proposes a tracking technique based on robust regression. Firstly, we introduce a novel robust linear regression estimator, which achieves favorable performance when the error vector follows i.i.d Gaussian-Laplacian distribution. Secondly, we design an iterative process to quickly solve the problem of outliers. In fact, the coefficients are obtained by Iterative Gradient Descent and Threshold Selection algorithm (IGDTS). In addition, we expend IGDTS to a generative tracker, and apply IGDTS-distance to measure the deviation between the sample and the model. Finally, we propose an update scheme to capture the appearance changes of the tracked object and ensure that the model is updated correctly. Experimental results on several challenging image sequences show that the proposed tracker outperformance existing trackers.