  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.
