Effective Motion Modeling for UAV-platform Multiple Object Tracking with Re-Margin Loss

• URL: http://arxiv.org/abs/2407.10485v1
• Date: Mon, 15 Jul 2024 07:13:27 GMT
• Title: Effective Motion Modeling for UAV-platform Multiple Object Tracking with Re-Margin Loss
• Authors: Mufeng Yao, Jinlong Peng, Qingdong He, Bo Peng, Hao Chen, Mingmin Chi, Chao Liu, Jon Atli Benediktsson,
• Abstract summary: Multiple object tracking from unmanned aerial vehicle platforms requires efficient motion modeling. We propose a flowing-by-detection module to realize accurate motion modeling with a minimum cost. Our proposed model can successfully track objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks.
• Score: 12.326023523101806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces tracking difficulties caused by large and irregular motion, and insufficient training due to the motion long-tailed distribution of current UAV-MOT datasets. Previous UAV-MOT methods either extract motion and detection features redundantly or supervise motion model in a sparse scheme, which limited their tracking performance and speed. To this end, we propose a flowing-by-detection module to realize accurate motion modeling with a minimum cost. Focusing on the motion long-tailed problem that were ignored by previous works, the flow-guided margin loss is designed to enable more complete training of large moving objects. Experiments on two widely open-source datasets show that our proposed model can successfully track objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks.

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