Summary of Awesome Multi-modal Object Tracking, by Chunhui Zhang et al.
Awesome Multi-modal Object Tracking
by Chunhui Zhang, Li Liu, Hao Wen, Xi Zhou, Yanfeng Wang
First submitted to arxiv on: 23 May 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 In this research paper, the authors investigate the rapidly growing field of Multi-modal Object Tracking (MMOT), which combines data from various modalities to estimate an object’s state in a video sequence. The authors categorize existing MMOT tasks into five main categories and analyze each task based on widely used datasets and mainstream tracking algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This technology has significant applications in autonomous driving and intelligent surveillance. To better understand the current state of MMOT, the paper provides a comprehensive overview of existing MMOT tasks, including RGBL, RGBE, RGBD, RGBT, and miscellaneous (RGB+X) tracking. The authors also summarize each task’s key features, datasets, and algorithms. |
Keywords
» Artificial intelligence » Multi modal » Object tracking » Tracking
