Summary of Predicting the Best Of N Visual Trackers, by Basit Alawode et al.
Predicting the Best of N Visual Trackers
by Basit Alawode, Sajid Javed, Arif Mahmood, Jiri Matas
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 presents a novel approach to visual tracking, addressing the issue of varying performance across different video attributes and datasets. The authors propose a meta-tracker called “Best of N Trackers” (BofN), which predicts the best-performing tracker for a given video sequence using only a few initial frames. This is achieved through a Tracking Performance Prediction Network (TP2N) based on self-supervised learning architectures such as MocoV2, SwAv, BT, and DINO. The TP2N is trained to select the most suitable tracker for each scenario, which is shown to outperform existing state-of-the-art (SOTA) trackers on nine standard benchmarks. Additionally, the paper provides an extensive evaluation of competitive tracking methods on all commonly used benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a method that helps predict the best-performing visual tracker for a video sequence, called “Best of N Trackers”. This is done by using a few initial frames and predicting which tracker will perform well. The authors test their approach on many benchmark videos and show that it works better than other methods. They also provide all the code and trained models so others can use them. |
Keywords
» Artificial intelligence » Self supervised » Tracking
