GL-DT: Multi-UAV Detection and Tracking with Global-Local Integration
This work addresses accurate and efficient multi-UAV tracking for applications like military reconnaissance and environmental monitoring, representing an incremental improvement over existing methods.
The paper tackled the problem of multi-object tracking for UAVs in complex environments with small targets and occlusions, proposing the GL-DT framework with a global-local detection strategy and JPTrack algorithm, which significantly improved tracking continuity and stability while maintaining real-time performance.
The extensive application of unmanned aerial vehicles (UAVs) in military reconnaissance, environmental monitoring, and related domains has created an urgent need for accurate and efficient multi-object tracking (MOT) technologies, which are also essential for UAV situational awareness. However, complex backgrounds, small-scale targets, and frequent occlusions and interactions continue to challenge existing methods in terms of detection accuracy and trajectory continuity. To address these issues, this paper proposes the Global-Local Detection and Tracking (GL-DT) framework. It employs a Spatio-Temporal Feature Fusion (STFF) module to jointly model motion and appearance features, combined with a global-local collaborative detection strategy, effectively enhancing small-target detection. Building upon this, the JPTrack tracking algorithm is introduced to mitigate common issues such as ID switches and trajectory fragmentation. Experimental results demonstrate that the proposed approach significantly improves the continuity and stability of MOT while maintaining real-time performance, providing strong support for the advancement of UAV detection and tracking technologies.