MM-Tracker: Motion Mamba with Margin Loss for UAV-platform Multiple Object Tracking
This work improves tracking for UAV applications, but it is incremental as it builds on existing motion modeling methods.
The paper tackles the problem of multiple object tracking from UAV platforms by addressing local and global motion modeling and motion blur, resulting in state-of-the-art performance on two open-source UAV-MOT datasets.
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.