Summary of Cross-view Referring Multi-object Tracking, by Sijia Chen et al.
Cross-View Referring Multi-Object Tracking
by Sijia Chen, En Yu, Wenbing Tao
First submitted to arxiv on: 23 Dec 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 This paper proposes a new task in the field of Referring Multi-Object Tracking (RMOT), which involves tracking objects that match language descriptions across multiple views. The current RMOT task is limited to single-view tracking, where some object appearances may be invisible, leading to incorrect matches. To address this issue, the authors introduce Cross-view Referring Multi-Object Tracking (CRMOT), which obtains object appearances from multiple views and maintains identity consistency. The authors construct a CRMOT benchmark based on CAMPUS and DIVOTrack datasets, with 13 scenes and 221 language descriptions. They also propose an end-to-end CRMOT method, CRTracker, which is evaluated on the benchmark. The dataset and code are available for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in tracking objects that match what people describe. Right now, we can only track objects from one angle or view. But sometimes things get hidden, so it’s hard to find them again. This new task, called CRMOT, helps by looking at multiple views and keeping track of which objects are the same. The researchers made a special test set with lots of scenes and descriptions for people to try their methods on. They also came up with a way to do this tracking that works well. |
Keywords
» Artificial intelligence » Object tracking » Tracking
