Open Access

ARTICLE

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

Yu-Shiuan Tsai^*, Yuk-Hang Sit

Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City, 202, Taiwan

* Corresponding Author: Yu-Shiuan Tsai. Email:

Computer Modeling in Engineering & Sciences 2025, 143(3), 3065-3090. https://doi.org/10.32604/cmes.2025.064783

Received 24 February 2025; Accepted 26 May 2025; Issue published 30 June 2025

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 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.

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Keywords

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