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Comparison of Performance of Different Background Subtraction Methods for Detection of Heavy Vehicles

机译:不同背景扣除方法在重型车辆检测中的性能比较

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The growing vehicle numbers in urban and national road networks emerged the need for effective monitoring and management of road traffic. Especially detecting vehicles with break average speed limits rules and trespassing a heavy vehicle is essential to constitute safety traffic flow. In the proposed study, the main goal was detecting heavy vehicles using surveillance videos by using interframe difference, approximate median filtering and Gaussian mixture models for background subtraction and compare their performance. Moreover, after removing the background image from original videos, on binary image morphological opening and blob analysis processes were applied and with minimum blob area of the detected object in a frame, heavy vehicle detection was achieved. Different background subtraction methods produce varying results, and these results were discussed. Our results were consistent with performance comparison studies which indicated the Gaussian mixture model was stable, real-time outdoor tracker in any varying outdoor condition.
机译:城市和国家道路网络中的车辆数量不断增加,因此需要有效监控和管理道路交通。尤其是检测具有平均速度限制规则的车辆并擅闯重型车辆对于构成安全交通流至关重要。在提出的研究中,主要目标是使用监控视频通过使用帧间差异,近似中值滤波和高斯混合模型进行背景减法来检测重型车辆,并比较其性能。此外,在从原始视频中去除背景图像之后,对二进制图像进行形态学打开和斑点分析处理,并以帧中检测到的对象的斑点区域最小,实现了重型车辆检测。不同的背景扣除方法会产生不同的结果,并讨论了这些结果。我们的结果与性能比较研究一致,后者表明高斯混合模型在任何变化的室外条件下都是稳定的实时户外跟踪器。

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