Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection
This work addresses precision issues in visual tracking for applications like surveillance or robotics, but it appears incremental as it builds on existing regression methods.
The paper tackles the problem of inaccuracies in visual tracking by proposing a robust regression-based technique with a novel estimator and iterative algorithm, achieving improved performance on challenging image sequences.
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.