TY  - EJOU
AU  - Tsai, Yu-Shiuan 
AU  - Sit, Yuk-Hang 

TI  - Aerial Object Tracking with Attention Mechanisms: Accurate Motion Path Estimation under Moving Camera Perspectives
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 3
SN  - 1526-1506

AB  - 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 spatial-temporal 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.
KW  - Aerial View Attention-PRB (AVA-PRB); aerial object tracking; small object detection; deep learning for Aerial vision; attention mechanisms in object detection; shape-IoU loss function; trajectory estimation; drone-based visual surveillance

DO  - 10.32604/cmes.2025.064783