Summary of Nt-vot211: a Large-scale Benchmark For Night-time Visual Object Tracking, by Yu Liu et al.
NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking
by Yu Liu, Arif Mahmood, Muhammad Haris Khan
First submitted to arxiv on: 27 Oct 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 presents NT-VOT211, a large-scale benchmark for evaluating visual object tracking algorithms in nighttime conditions. The dataset consists of 211 videos with 211,000 well-annotated frames and eight attributes that simulate real-world challenges like camera motion, deformation, and occlusion. The authors analyze the performance of 42 diverse tracking algorithms on NT-VOT211, uncovering strengths and limitations, and highlighting opportunities for enhancements in visual object tracking. A leaderboard is provided to rank algorithm performance, along with annotation tools, meta-information, and reproducibility code. The paper aims to facilitate field deployment of VOT algorithms and unlock new real-world tracking applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a special dataset that helps computers track moving objects at night. Currently, there are few challenges designed for this task, so the authors created one with 211 videos and millions of frames. They tested many different computer programs on this challenge and found out which ones work well or poorly in nighttime conditions. This will help improve object tracking algorithms and open up new possibilities for applications like security cameras. |
Keywords
» Artificial intelligence » Object tracking » Tracking
