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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13950 |
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| _version_ | 1866929223595196416 |
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| author | Kim, Vitaliy Jung, Gunho Lee, Seong-Whan |
| author_facet | Kim, Vitaliy Jung, Gunho Lee, Seong-Whan |
| contents | Many multi-object tracking (MOT) approaches, which employ the Kalman Filter as a motion predictor, assume constant velocity and Gaussian-distributed filtering noises. These assumptions render the Kalman Filter-based trackers effective in linear motion scenarios. However, these linear assumptions serve as a key limitation when estimating future object locations within scenarios involving non-linear motion and occlusions. To address this issue, we propose a motion-based MOT approach with an adaptable motion predictor, called AM-SORT, which adapts to estimate non-linear uncertainties. AM-SORT is a novel extension of the SORT-series trackers that supersedes the Kalman Filter with the transformer architecture as a motion predictor. We introduce a historical trajectory embedding that empowers the transformer to extract spatio-temporal features from a sequence of bounding boxes. AM-SORT achieves competitive performance compared to state-of-the-art trackers on DanceTrack, with 56.3 IDF1 and 55.6 HOTA. We conduct extensive experiments to demonstrate the effectiveness of our method in predicting non-linear movement under occlusions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking Kim, Vitaliy Jung, Gunho Lee, Seong-Whan Computer Vision and Pattern Recognition Many multi-object tracking (MOT) approaches, which employ the Kalman Filter as a motion predictor, assume constant velocity and Gaussian-distributed filtering noises. These assumptions render the Kalman Filter-based trackers effective in linear motion scenarios. However, these linear assumptions serve as a key limitation when estimating future object locations within scenarios involving non-linear motion and occlusions. To address this issue, we propose a motion-based MOT approach with an adaptable motion predictor, called AM-SORT, which adapts to estimate non-linear uncertainties. AM-SORT is a novel extension of the SORT-series trackers that supersedes the Kalman Filter with the transformer architecture as a motion predictor. We introduce a historical trajectory embedding that empowers the transformer to extract spatio-temporal features from a sequence of bounding boxes. AM-SORT achieves competitive performance compared to state-of-the-art trackers on DanceTrack, with 56.3 IDF1 and 55.6 HOTA. We conduct extensive experiments to demonstrate the effectiveness of our method in predicting non-linear movement under occlusions. |
| title | AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.13950 |