A reliable unmanned aerial vehicle multi-target tracking system with global motion compensation for monitoring Procapra przewalskii

Procapra przewalskii, which inhabits plateau areas, faces the constant threat of poaching and unpredictable risks that impede its survival. The implementation of a comprehensive, real-time monitoring and tracking system for Procapra przewalskii using artificial intelligence and unmanned aerial vehicle (UAV) technology is crucial to safeguard its existence. Therefore, a UAV multi-object-tracking (MOT) system with global motion compensation (GMC) was proposed in this study. YOLOv7 and Deep SORT were employed for object detection and tracking, respectively. Furthermore, the Kalman filter (KF) in Deep SORT is optimized to enhance the accuracy of object-tracking. Moreover, a novel appearance feature-extraction network (FEN) is introduced to enable more effective multi-scale feature (MSF) extraction. In addition, a GMC module was proposed to align neighboring frames through feature matching. This facilitates the correction of the position of the target in the subsequent frame, mitigating the impact of UAV camera motion on tracking. The results demonstrated the remarkable tracking accuracy of the system. Compared with the Deep SORT model, the proposed system exhibited an increase of 6.4% in MOTA, 2.7% in MOTP, and 7.9% in IDF1. Through a comprehensive evaluation and analysis of real-world tracking scenarios, the system proposed in this study exhibits reliability in complex scenes and holds the potential to significantly enhance the protection of Procapra przewalskii from threats.