View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910014722015232 |
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| author | Ji, Deyi Zhu, Lanyun Gao, Siqi Zhu, Qi Zhao, Yiru Xu, Peng Ding, Yue Lu, Hongtao Ye, Jieping Wu, Feng Zhao, Feng |
| author_facet | Ji, Deyi Zhu, Lanyun Gao, Siqi Zhu, Qi Zhao, Yiru Xu, Peng Ding, Yue Lu, Hongtao Ye, Jieping Wu, Feng Zhao, Feng |
| contents | In this paper, we address the challenge of Multi-Object Tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT. Specifically, changes in the scene background not only render traditional frame-to-frame object IoU association methods ineffective but also introduce significant view shifts in the objects, which complicates tracking. To overcome these issues, we propose a novel HomView-MOT framework, which for the first time, harnesses the view homography inherent in changing scenes to solve MOT challenges in moving environments, incorporating homographic matching and view-centric concepts. We introduce a Fast Homography Estimation (FHE) algorithm for rapid computation of homography matrices between video frames, enabling object View-Centric ID Learning (VCIL) and leveraging multi-view homography to learn cross-view ID features. Concurrently, our Homographic Matching Filter (HMF) maps object bounding boxes from different frames onto a common view plane for a more realistic physical IoU association. Extensive experiments have proven that these innovations allow HomView-MOT to achieve state-of-the-art performance on prominent UAV MOT datasets VisDrone and UAVDT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10830 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV Ji, Deyi Zhu, Lanyun Gao, Siqi Zhu, Qi Zhao, Yiru Xu, Peng Ding, Yue Lu, Hongtao Ye, Jieping Wu, Feng Zhao, Feng Computer Vision and Pattern Recognition In this paper, we address the challenge of Multi-Object Tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT. Specifically, changes in the scene background not only render traditional frame-to-frame object IoU association methods ineffective but also introduce significant view shifts in the objects, which complicates tracking. To overcome these issues, we propose a novel HomView-MOT framework, which for the first time, harnesses the view homography inherent in changing scenes to solve MOT challenges in moving environments, incorporating homographic matching and view-centric concepts. We introduce a Fast Homography Estimation (FHE) algorithm for rapid computation of homography matrices between video frames, enabling object View-Centric ID Learning (VCIL) and leveraging multi-view homography to learn cross-view ID features. Concurrently, our Homographic Matching Filter (HMF) maps object bounding boxes from different frames onto a common view plane for a more realistic physical IoU association. Extensive experiments have proven that these innovations allow HomView-MOT to achieve state-of-the-art performance on prominent UAV MOT datasets VisDrone and UAVDT. |
| title | View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.10830 |