AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking

• URL: http://arxiv.org/abs/2401.13950v1
• Date: Thu, 25 Jan 2024 05:09:30 GMT
• Title: AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking
• Authors: Vitaliy Kim, Gunho Jung, and Seong-Whan Lee
• Abstract summary: 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.
• Score: 26.585985828583304
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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