Summary of Robust Visual Tracking Via Iterative Gradient Descent and Threshold Selection, by Zhuang Qi et al.
Robust Visual Tracking via Iterative Gradient Descent and Threshold Selection
by Zhuang Qi, Junlin Zhang, Xin Qi
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a robust regression-based tracking technique to enhance the precision of target estimation in visual tracking. The authors introduce a novel robust linear regression estimator, which achieves favorable performance when errors follow an i.i.d Gaussian-Laplacian distribution. They also design an iterative process to quickly solve outlier problems using Iterative Gradient Descent and Threshold Selection (IGDTS) algorithm. This is extended to a generative tracker with IGDTS-distance measuring deviation between samples and models. The authors also propose an update scheme to capture appearance changes of tracked objects. Experimental results show that the proposed tracker outperforms existing trackers on several challenging image sequences, showcasing its robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make visual tracking better by using a new way to predict where things are in each frame of a video. Right now, most trackers have trouble estimating the target’s state because they’re not good at dealing with mistakes or unexpected changes. To fix this, the authors came up with a new method that uses “robust regression” to make more accurate predictions. They also developed an algorithm called IGDTS (Iterative Gradient Descent and Threshold Selection) to handle outliers and another way to measure how different samples are from each other. Finally, they created an update scheme to keep the tracker’s model up-to-date as things change in the video. The results show that this new approach does a better job than existing trackers at tracking objects in challenging situations. |
Keywords
» Artificial intelligence » Gradient descent » Linear regression » Precision » Regression » Tracking
