Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring

Kristina Telegraph;Christos Kyrkou
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引用次数: 0

Abstract

This work presents advancements in multiclass vehicle detection using unmanned aerial vehicle (UAV) cameras through the development of spatiotemporal object detection models. The study introduces a spatiotemporal vehicle detection dataset (STVD) containing $6600$ annotated sequential frame images captured by UAVs, enabling comprehensive training and evaluation of algorithms for holistic spatiotemporal perception. A YOLO-based object detection algorithm is enhanced to incorporate temporal dynamics, resulting in improved performance over single frame models. The integration of attention mechanisms into spatiotemporal models is shown to further enhance performance. Experimental validation demonstrates significant progress, with the best spatiotemporal model exhibiting a 16.22% improvement over single frame models, while it is demonstrated that attention mechanisms hold the potential for additional performance gains.
交通监控中改进飞行器检测的时空目标检测
这项工作通过开发时空目标检测模型,介绍了使用无人机(UAV)相机进行多类别车辆检测的进展。该研究引入了一个时空车辆检测数据集(STVD),其中包含由无人机捕获的6600张带注释的序列帧图像,能够对整体时空感知算法进行全面的训练和评估。一种基于yolo的目标检测算法被增强,以结合时间动态,从而提高了单帧模型的性能。将注意机制整合到时空模型中可以进一步提高表现。实验验证显示了显著的进步,与单帧模型相比,最佳时空模型表现出16.22%的改进,同时表明注意机制具有额外性能提升的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.70
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0.00%
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