Papers › RetinaTrack: Online Single Stage Joint Detection and Tracking

RetinaTrack: Online Single Stage Joint Detection and Tracking

30 Mar 2020CVPR 2020 6arXiv:2003.13870archive 2025-07-28

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

Hanson0910/RetinaTrack mentioned on GitHubpytorch report

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Tasks

Autonomous DrivingMulti-Object TrackingMultiple Object TrackingObject DetectionObject Trackingobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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Methods

1x1 ConvolutionConvolutionFPNFocal LossRetinaNet

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