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Robust vehicle detection and tracking method for Blind Spot Detection System by using vision sensors

机译:视觉传感器的盲点检测系统的鲁棒车辆检测与跟踪方法

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This paper presents a method to detect the present vehicles from side and rear for BSDS(Blind Spot Detection System) with vision system. Because the real image acquired during car driving has a lot of information to exam the target vehicle, background image, and the noises such as lighting and shading, it is hard to extract only the target vehicle for the background image with satisfied robustness. In this paper, the target vehicle is detected by repetitive image processing such as sobel and morphological operations and a Kalman filter is also designed to cancel the background image and prevent the misreading of the target image. Compared to previous researches, the proposed method can get an image processing with much improved speed and robustness. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.
机译:本文提出了一种带有视觉系统的BSDS(盲点检测系统)从侧面和背面检测当前车辆的方法。由于在汽车驾驶过程中获取的真实图像具有大量信息来检查目标车辆,背景图像以及诸如照明和阴影之类的噪声,因此很难为背景图像仅提取目标车辆并具有令人满意的鲁棒性。在本文中,目标车辆通过重复图像处理(例如sobel和形态学运算)进行检测,并且还设计了卡尔曼滤波器以消除背景图像并防止目标图像误读。与以前的研究相比,该方法可以得到速度和鲁棒性大大提高的图像处理。在高速公路驾驶情况下进行了各种实验,以评估该算法的性能。

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