Multi-UAV Multi-Object Tracking Based on Deep Learning

• الحاوية / القاعدة: Jisuanji gongcheng
• المؤلف الرئيسي: ZHOU Hanqi, FANG Dongxu, ZHANG Ningbo, SUN Wensheng
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: Editorial Office of Computer Engineering 2025-04-01
• الموضوعات: unmanned aerial vehicle (uav)|occluded object|multi-uav tracking|multi-object tracking (mot)|object association
• الوصول للمادة أونلاين: https://www.ecice06.com/fileup/1000-3428/PDF/20250406.pdf

Unmanned Aerial Vehicle (UAV) Multi-Object Tracking (MOT) technology is widely used in various fields such as traffic operation, safety monitoring, and water area inspection. However, existing MOT algorithms are primarily designed for single-UAV MOT scenarios. The perspective of a single-UAV typically has certain limitations, which can lead to tracking failures when objects are occluded, thereby causing ID switching. To address this issue, this paper proposes a Multi-UAV Multi-Object Tracking (MUMTTrack) algorithm. The MUMTTrack network adopts an MOT paradigm based on Tracking By Detection (TBD), utilizing multiple UAVs to track objects simultaneously and compensating for the perspective limitations of a single-UAV. Additionally, to effectively integrate the tracking results from multiple UAVs, an ID assignment strategy and an image matching strategy are designed based on the Speeded Up Robust Feature (SURF) algorithm for MUMTTrack. Finally, the performance of MUMTTrack is compared with that of existing widely used single-UAV MOT algorithms on the MDMT dataset. According to the comparative analysis, MUMTTrack demonstrates significant advantages in terms of MOT performance metrics, such as the Identity F1 (IDF1) value and Multi-Object Tracking Accuracy (MOTA).