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There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object tracking.
Ranked #1 on Multi-Object Tracking on MOT16 (using extra training data)
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #6 on Multi-Object Tracking on MOT16 (using extra training data)
Nowadays, tracking is dominated by pipelines that perform object detection followed by temporal association, also known as tracking-by-detection.
Ranked #2 on Multiple Object Tracking on KITTI Tracking test
Additionally, 3D MOT datasets such as KITTI evaluate MOT methods in the 2D space and standardized 3D MOT evaluation tools are missing for a fair comparison of 3D MOT methods.
Ranked #2 on 3D Multi-Object Tracking on KITTI
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #6 on Keypoint Detection on COCO test-challenge
Online multi-object tracking is a fundamental problem in time-critical video analysis applications.
Ranked #2 on Online Multi-Object Tracking on MOT16
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #4 on Multi-Object Tracking on 2D MOT 2015
Despite the fact that the two components are dependent on each other, prior works often design detection and data association modules separately which are trained with separate objectives.
Ranked #1 on Multi-Object Tracking on MOT20