MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking

שמור ב:
מידע ביבליוגרפי
הוצא לאור ב:arXiv.org (Dec 12, 2024), p. n/a
מחבר ראשי: Yao, Mufeng
מחברים אחרים: Peng, Jinlong, He, Qingdong, Peng, Bo, Chen, Hao, Chi, Mingmin, Liu, Chao, Jon Atli Benediktsson
יצא לאור:
Cornell University Library, arXiv.org
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גישה מקוונת:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3081438670 
045 0 |b d20241212 
100 1 |a Yao, Mufeng 
245 1 |a MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking 
260 |b Cornell University Library, arXiv.org  |c Dec 12, 2024 
513 |a Working Paper 
520 3 |a Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms requires efficient motion modeling. This is because UAV-MOT faces both local object motion and global camera motion. Motion blur also increases the difficulty of detecting large moving objects. Previous UAV motion modeling approaches either focus only on local motion or ignore motion blurring effects, thus limiting their tracking performance and speed. To address these issues, we propose the Motion Mamba Module, which explores both local and global motion features through cross-correlation and bi-directional Mamba Modules for better motion modeling. To address the detection difficulties caused by motion blur, we also design motion margin loss to effectively improve the detection accuracy of motion blurred objects. Based on the Motion Mamba module and motion margin loss, our proposed MM-Tracker surpasses the state-of-the-art in two widely open-source UAV-MOT datasets. Code will be available. 
653 |a Feature extraction 
653 |a Datasets 
653 |a Multiple target tracking 
653 |a Motion perception 
653 |a Unmanned aerial vehicles 
653 |a Modelling 
653 |a Moving object recognition 
653 |a Minimum cost 
700 1 |a Peng, Jinlong 
700 1 |a He, Qingdong 
700 1 |a Peng, Bo 
700 1 |a Chen, Hao 
700 1 |a Chi, Mingmin 
700 1 |a Liu, Chao 
700 1 |a Jon Atli Benediktsson 
773 0 |t arXiv.org  |g (Dec 12, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3081438670/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2407.10485