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Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos

机译：基于航空视频车辆检测与跟踪的车辆计数

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摘要

Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively.
机译：无人飞行器（UAV）的飞行器计数已成为交通监控中的热门研究主题。安装在无人机上的摄像机可以看作是用于收集航拍视频的视觉传感器。与传统传感器相比，无人机可以灵活地部署到需要监视的区域，并且可以提供更大的视野。本文提出了一种基于航拍视频的车辆计数新框架。在我们的框架中，运动对象检测器可以处理以下两种情况：静态背景和运动背景。对于静态背景，给出了像素级视频前景检测器来检测车辆，车辆可以连续更新背景模型。对于运动的背景，图像配准用于估计摄像机的运动，这允许在参考坐标系中检测车辆。此外，为了克服图像中车辆的比例和形状的变化，我们采用了在线学习跟踪器，可以更新用于训练的样本。最后，我们设计了一个多目标管理模块，该模块可以使用多线程技术有效地分析和验证被跟踪车辆的状态。我们的方法已在包含固定背景和运动背景的真实高速公路场景的航拍视频上进行了测试。实验结果表明，所提方法在固定背景视频和动态背景视频中的车辆计数精度分别达到90％和85％以上。

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