Papers › RetinaTrack: Online Single Stage Joint Detection and Tracking
RetinaTrack: Online Single Stage Joint Detection and Tracking
Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan Huang
Traditionally multi-object tracking and object detection are performed using separate systems with most prior works focusing exclusively on one of these aspects over the other. Tracking systems clearly benefit from having access to accurate detections, however and there is ample evidence in literature that detectors can benefit from tracking which, for example, can help to smooth predictions over time. In this paper we focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach such that it is amenable to instance-level embedding training. We show, via evaluations on the Waymo Open Dataset, that we outperform a recent state of the art tracking algorithm while requiring significantly less computation. We believe that our simple yet effective approach can serve as a strong baseline for future work in this area.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multiple Object Tracking | Waymo Open Dataset | RetinaTrack | Category | Vehicle | #2 of 2 | Archive leaderboard | report |
| Multiple Object Tracking | Waymo Open Dataset | RetinaTrack | MOTA | 44.92 | #2 of 2 | Archive leaderboard | report |
| Multiple Object Tracking | Waymo Open Dataset | RetinaTrack | mAP | 45.70 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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