RetinaTrack: Online Single Stage Joint Detection and Tracking
Open accessAbstract
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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In robotics, single-stage detectors assist in real-time object recognition, facilitating navigation and interaction with the environment.
“In robotics, these detectors assist in real-time object recognition, facilitating navigation and interaction with the environment [30].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [30]
Cites
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FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
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Detection and tracking meet drones challenge
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Quasi-Dense Similarity Learning for Multiple Object Tracking
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A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism
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“[35] established the multi-target vehicle tracking model RetinaTrack to integrate vehicle motion characteristics and appearance characteristics for data association.”
Method
- Authors
- Lu, Z., Rathod, V., Votel, R., & Huang, J.
- Affiliations
- Google (United States) (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 14656-14666
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035410385
- Cited by
- 254
- References
- 14
- Citations by year
- 2026: 9 · 2025: 33 · 2024: 46 · 2023: 63 · 2022: 57 · 2021: 37 · 2020: 9
- FWCI
- 12.80
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, object detection, joint detection and tracking, tracking-by-detection, RetinaNet, one-stage object detection, autonomous driving, Waymo Open Dataset, online tracking
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