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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network
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Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network

机译：基于超高分辨率无人机视频和深度神经网络的城市交通密度估计

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摘要

This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512 x 512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation.
机译：本文提出了一种先进的城市交通密度估算解决方案，该解决方案使用最新的深度学习技术来智能处理从无人机（UAV）拍摄的超高分辨率交通视频。我们首先通过在繁忙时间驾驶无人驾驶飞机在现代大城市的五个繁忙道路交叉口拍摄近一个小时的超高分辨率交通视频。然后，我们从视频帧中随机采样了17 K 512 x 512像素的图像块，并手动注释了64 K车辆，以形成本文的数据集，该数据集也将提供给研究团体以用于研究目的。我们创新的城市交通分析解决方案包括基于高级深度神经网络（DNN）的车辆检测和定位，类型（汽车，公交车和卡车）的识别，跟踪以及随时间推移的车辆计数。我们将提供广泛的实验结果，以证明我们的解决方案的有效性。我们将展示我们的增强型单发多盒检测器（Enhanced-SSD）优于其他基于DNN的技术，并且在交通视频分析中，深度学习技术比传统的计算机视觉技术更有效。我们还将展示，超高分辨率视频提供的信息比低分辨率内容更多，从而可以更准确地检测和识别车辆。本文不仅展示了使用最新技术进步（超高分辨率视频和UAV）的优势，而且还提供了基于DNN的先进解决方案，可以利用这些技术进步进行城市交通密度估算。

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