Aerial Object Tracking with Attention Mechanisms: Accurate Motion Path Estimation under Moving Camera Perspectives

Abstract

To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms-Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)-to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person Detection dataset, and 51.2% mAP@0.5 on VisDrone2019-Det, demonstrating its effectiveness in aerial object detection. Beyond detection, we propose a novel trajectory estimation method that improves movement path prediction under aerial motion. Experimental results indicate that our approach reduces path deviation by up to 64%, effectively mitigating errors caused by rapid camera movements and background variations. By optimizing feature extraction and enhancing spatialtemporal coherence, our method significantly improves object tracking under aerial moving perspectives. This research addresses the limitations of fixed-camera tracking, enhancing flexibility and accuracy in aerial tracking applications. The proposed approach has broad potential for real-world applications, including surveillance, traffic monitoring, and environmental observation.