Summary of Solution For Point Tracking Task Of Iccv 1st Perception Test Challenge 2023, by Hongpeng Pan et al.
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023
by Hongpeng Pan, Yang Yang, Zhongtian Fu, Yuxuan Zhang, Shian Du, Yi Xu, Xiangyang Ji
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 report proposes an improved method for tracking any physical surface through a video, addressing cumulative error issues in existing approaches. The proposed TAP with confident static points (TAPIR+) method focuses on rectifying the tracking of static points in videos shot by a static camera. This is achieved through two key components: Multi-granularity Camera Motion Detection and CMR-based point trajectory prediction with one moving object segmentation. The approach outperformed others, ranking first in the final test with a score of 0.46. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This report makes it easier to track physical surfaces in videos. It’s like trying to follow someone walking through a park – you want to get an accurate picture of where they are and what they’re doing. Current methods for this task have some problems, like making mistakes because they predict the future instead of looking at what’s actually happening. To fix this, the researchers came up with a new approach that focuses on getting the static parts right, like when the camera is stationary. They also developed ways to detect motion and separate moving objects from still ones. This method performed well in tests, showing it’s a step forward in tracking technology. |
Keywords
* Artificial intelligence * Tracking
