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Learning to Track with Object Permanence
Open accessAbstract
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the quality of instantaneous observations, often failing when objects are not fully visible. In contrast, tracking in humans is underlined by the notion of object permanence: once an object is recognized, we are aware of its physical existence and can approximately localize it even under full occlusions. In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning. We build on top of the recent CenterTrack architecture, which takes pairs of frames as input, and extend it to videos of arbitrary length. To this end, we augment the model with a spatio-temporal, recurrent memory module, allowing it to reason about object locations and identities in the current frame using all the previous history. It is, however, not obvious how to train such an approach. We study this question on a new, large-scale, synthetic dataset for multi-object tracking, which provides ground truth annotations for invisible objects, and propose several approaches for supervising tracking behind occlusions. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI and MOT17 datasets thanks to its robustness to occlusions.
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“However, the projection of non-linear 3D motion [50] into the 2D image domain still poses a challenging problem for many models.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [50]
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“Only [50,53,54] which follow [66] and pretrain on 6 additional tracking datasets (6M) surpass our performance.”
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Hoorick, B. V., Tokmakov, P., Stent, S., Li, J., & Vondrick, C. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01326 · Cited by 14
Self-citationA deep learning framework for multi‐object tracking in team sports videos
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OMCTrack: Integrating Occlusion Perception and Motion Compensation for UAV Multi-Object Tracking
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Nagy, M., Khonji, M., Dias, J. M. M., & Javed, S. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160328 · Cited by 13
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Hu, Y., Sun, J., Jin, H., Niu, A., Yan, Q., Zhu, Y., & Zhang, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3558588 · Cited by 12
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers
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Leveraging temporal-aware fine-grained features for robust multiple object tracking
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Metadata
- Authors
- Tokmakov, P., Li, J., Burgard, W., & Gaidon, A.
- Affiliations
- Toyota Motor Corporation (Switzerland) (CH)
- University of Freiburg (DE)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10840-10849
- OpenAlex
- W3142617676
- Cited by
- 226
- References
- 74
- Citations by year
- 2026: 14 · 2025: 43 · 2024: 72 · 2023: 48 · 2022: 46 · 2021: 3
- FWCI
- 13.74
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE/CVF International Conference on Computer Vision (ICCV)
- 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 permanence, occlusion handling, joint detection and tracking, spatiotemporal memory, end-to-end tracking, KITTI, MOT17
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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