Abstract
This paper concerns the problem of multi-object tracking based on the min-cost flow (MCF) formulation, which is conventionally studied as an instance of linear program. Given its computationally tractable inference, the success of MCF tracking largely relies on the learned cost function of underlying linear program. Most previous studies focus on learning the cost function by only taking into account two frames during training, therefore the learned cost function is sub-optimal for MCF where a multi-frame data association must be considered during inference. In order to address this problem, in this paper we propose a novel differentiable framework that ties training and inference to-gether during learning by solving a bi-level optimization problem, where the lower-level solves a linear program and the upper-level contains a loss function that incorpo-rates global tracking result. By back-propagating the loss through differentiable layers via gradient descent, the glob-ally parameterized cost function is explicitly learned and regularized. With this approach, we are able to learn a better objective for global MCF tracking. As a result, we achieve competitive performances compared to the current state-of-the-art methods on the popular multi-object tracking benchmarks such as MOT16, MOT17 and MOT20.
Original language | English |
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Title of host publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 |
Editors | Kristin Dana, Gang Hua, Stefan Roth, Dimitris Samaras, Richa Singh |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 8845-8855 |
Number of pages | 11 |
ISBN (Electronic) | 9781665469463 |
DOIs | |
Publication status | Published - 2022 |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2022 - New Orleans, United States of America Duration: 19 Jun 2022 → 24 Jun 2022 https://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding (Proceedings) https://cvpr2022.thecvf.com https://cvpr2022.thecvf.com/ (Website) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2022-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2022 |
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Abbreviated title | CVPR 2022 |
Country/Territory | United States of America |
City | New Orleans |
Period | 19/06/22 → 24/06/22 |
Internet address |
Keywords
- Motion and tracking
- Pose estimation and tracking
- Scene analysis and understanding
- Video analysis and understanding