Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914730171432960 |
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| author | Pan, Hongpeng Yang, Yang Fu, Zhongtian Zhang, Yuxuan Du, Shian Xu, Yi Ji, Xiangyang |
| author_facet | Pan, Hongpeng Yang, Yang Fu, Zhongtian Zhang, Yuxuan Du, Shian Xu, Yi Ji, Xiangyang |
| contents | This report proposes an improved method for the Tracking Any Point (TAP) task, which tracks any physical surface through a video. Several existing approaches have explored the TAP by considering the temporal relationships to obtain smooth point motion trajectories, however, they still suffer from the cumulative error caused by temporal prediction. To address this issue, we propose a simple yet effective approach called TAP with confident static points (TAPIR+), which focuses on rectifying the tracking of the static point in the videos shot by a static camera. To clarify, our approach contains two key components: (1) Multi-granularity Camera Motion Detection, which could identify the video sequence by the static camera shot. (2) CMR-based point trajectory prediction with one moving object segmentation approach to isolate the static point from the moving object. Our approach ranked first in the final test with a score of 0.46. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023 Pan, Hongpeng Yang, Yang Fu, Zhongtian Zhang, Yuxuan Du, Shian Xu, Yi Ji, Xiangyang Computer Vision and Pattern Recognition Machine Learning This report proposes an improved method for the Tracking Any Point (TAP) task, which tracks any physical surface through a video. Several existing approaches have explored the TAP by considering the temporal relationships to obtain smooth point motion trajectories, however, they still suffer from the cumulative error caused by temporal prediction. To address this issue, we propose a simple yet effective approach called TAP with confident static points (TAPIR+), which focuses on rectifying the tracking of the static point in the videos shot by a static camera. To clarify, our approach contains two key components: (1) Multi-granularity Camera Motion Detection, which could identify the video sequence by the static camera shot. (2) CMR-based point trajectory prediction with one moving object segmentation approach to isolate the static point from the moving object. Our approach ranked first in the final test with a score of 0.46. |
| title | Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023 |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.17994 |