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.

基于鲁棒回归的视觉跟踪技术中，引入了一种新的鲁棒线性回归估计器，通过迭代梯度下降和阈值选择算法解决异常值问题，同时将算法扩展到生成式跟踪器，并使用 IGDTS 距离来衡量样本与模型之间的偏差，最后提出了一种更新方案以捕捉跟踪目标的外观变化并确保正确更新模型，实验结果表明所提出的跟踪器优于现有的跟踪器。