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Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos

机译：基于车辆检测和航空视频跟踪的车辆计数

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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：从无人驾驶飞行器（UAV）计算的车辆正在成为流量监控的流行研究主题。安装在UAV上的相机可被视为用于收集空中视频的可视传感器。与传统传感器相比，UAV可以灵活地部署到需要监控的区域，并且可以提供更大的角度。本文提出了一种基于天线录像的车辆计数的新框架。在我们的框架中，移动对象探测器可以处理以下两种情况：静态背景和移动背景。对于静态背景，给出像素级视频前景检测器来检测可以连续更新背景模型的车辆。对于移动背景，采用图像登记来估计相机运动，这允许在参考坐标系中检测到车辆。此外，为了克服图像中车辆的规模和形状的变化，我们采用了一个在线学习跟踪器，可以更新用于训练的样本。最后，我们设计了一种多对象管理模块，可以有效地分析和验证跟踪车辆的状态，具有多线程技术。我们的方法在含有固定背景和移动背景的真正公路场景的空中视频上进行了测试。实验结果表明，该方法分别可以分别达到固定背景视频和移动背景视频中的车辆数量超过90％和85％的准确性。

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