The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
Abstract
Object detection and data association are critical components in multi-object tracking (MOT) systems. 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. As a result, one cannot back-propagate the gradients and optimize the entire MOT system, which leads to sub-optimal performance. To address this issue, recent works simultaneously optimize detection and data association modules under a joint MOT framework, which has shown improved performance in both modules. In this work, we propose a new instance of joint MOT approach based on Graph Neural Networks (GNNs). The key idea is that GNNs can model relations between variablesized objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association. Through extensive experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of our GNN-based joint MOT approach and show state-of-the-art performance for both detection and MOT tasks.
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Cites
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Cited by
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
Self-citationTrackFormer: Multi-Object Tracking with Transformers
Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00864 · Cited by 1,081
“However, we surpass or perform on-par with several modern methods [55,66,68] that were trained on significantly more data, i.”
MethodMOTR: End-to-End Multiple-Object Tracking with Transformer
Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19812-0_38 · Cited by 752
Metadata
- Authors
- Wang, Y., Kitani, K., & Weng, X.
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 13708-13715
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3207452968
- Cited by
- 291
- References
- 85
- FWCI
- 19.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, Y., Kitani, K., & Weng, X. (2021). Joint Object Detection and Multi-Object Tracking with Graph Neural Networks. 2021 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra48506.2021.9561110
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