EgoPoints: Advancing Point Tracking for Egocentric Videos
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915050272325632 |
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| author | Darkhalil, Ahmad Guerrier, Rhodri Harley, Adam W. Damen, Dima |
| author_facet | Darkhalil, Ahmad Guerrier, Rhodri Harley, Adam W. Damen, Dima |
| contents | We introduce EgoPoints, a benchmark for point tracking in egocentric videos. We annotate 4.7K challenging tracks in egocentric sequences. Compared to the popular TAP-Vid-DAVIS evaluation benchmark, we include 9x more points that go out-of-view and 59x more points that require re-identification (ReID) after returning to view. To measure the performance of models on these challenging points, we introduce evaluation metrics that specifically monitor tracking performance on points in-view, out-of-view, and points that require re-identification. We then propose a pipeline to create semi-real sequences, with automatic ground truth. We generate 11K such sequences by combining dynamic Kubric objects with scene points from EPIC Fields. When fine-tuning point tracking methods on these sequences and evaluating on our annotated EgoPoints sequences, we improve CoTracker across all metrics, including the tracking accuracy $δ^\star_{\text{avg}}$ by 2.7 percentage points and accuracy on ReID sequences (ReID$δ_{\text{avg}}$) by 2.4 points. We also improve $δ^\star_{\text{avg}}$ and ReID$δ_{\text{avg}}$ of PIPs++ by 0.3 and 2.8 respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EgoPoints: Advancing Point Tracking for Egocentric Videos Darkhalil, Ahmad Guerrier, Rhodri Harley, Adam W. Damen, Dima Computer Vision and Pattern Recognition We introduce EgoPoints, a benchmark for point tracking in egocentric videos. We annotate 4.7K challenging tracks in egocentric sequences. Compared to the popular TAP-Vid-DAVIS evaluation benchmark, we include 9x more points that go out-of-view and 59x more points that require re-identification (ReID) after returning to view. To measure the performance of models on these challenging points, we introduce evaluation metrics that specifically monitor tracking performance on points in-view, out-of-view, and points that require re-identification. We then propose a pipeline to create semi-real sequences, with automatic ground truth. We generate 11K such sequences by combining dynamic Kubric objects with scene points from EPIC Fields. When fine-tuning point tracking methods on these sequences and evaluating on our annotated EgoPoints sequences, we improve CoTracker across all metrics, including the tracking accuracy $δ^\star_{\text{avg}}$ by 2.7 percentage points and accuracy on ReID sequences (ReID$δ_{\text{avg}}$) by 2.4 points. We also improve $δ^\star_{\text{avg}}$ and ReID$δ_{\text{avg}}$ of PIPs++ by 0.3 and 2.8 respectively. |
| title | EgoPoints: Advancing Point Tracking for Egocentric Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.04592 |