# Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection

arXiv: https://arxiv.org/abs/2406.00589 · HTML: https://mlsanity.com/paper/2406.00589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2308.03457/) (2023)
- [Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving](https://mlsanity.com/paper/2205.14882/) (2022)
- [DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking](https://mlsanity.com/paper/1503.00072/) (2015)

## Built on by (most cited first, 1 of 1)

- [Federated Out-of-Distribution Generalization: A Causal Augmentation View](https://mlsanity.com/paper/2504.19882/) (2025)

## Abstract (arXiv)

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.