Summary of Mot Fcg++: Enhanced Representation Of Spatio-temporal Motion and Appearance Features, by Yanzhao Fang
MOT FCG++: Enhanced Representation of Spatio-temporal Motion and Appearance Features
by Yanzhao Fang
First submitted to arxiv on: 15 Nov 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 proposed approach improves multi-object tracking (MOT) by effectively representing spatial-temporal motion features and appearance embedding features of detected objects in consecutive frames. The method builds upon the hierarchical clustering association method MOT FCG, introducing Diagonal Modulated GIoU for spatial-temporal motion feature representation and Mean Constant Velocity Modeling to reduce observation noise on target motion state estimation. Additionally, a dynamic appearance representation is used, incorporating confidence information for more robust and global trajectory appearance features. Experimental results show significant improvements in performance on the MOT17 test set, achieving 63.1 HOTA, 76.9 MOTA, and 78.2 IDF1, with competitive results on MOT20 and DanceTrack sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to track objects that move around in a scene. Right now, most tracking methods only look at the position of an object over time, but this method also considers how the shape of the object changes as it moves. This helps reduce errors caused by things like lighting or camera movement. The approach is called Diagonal Modulated GIoU and Mean Constant Velocity Modeling, and it makes the tracking more accurate and reliable. |
Keywords
» Artificial intelligence » Embedding » Hierarchical clustering » Object tracking » Tracking
