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A Video Vehicle Tracking Algorithm Based on Ploar Fourier Descriptor

机译:基于极傅立叶描述子的视频车辆跟踪算法

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Video-based vehicle tracking movement in the field of intelligent transportation is an important research question. This paper presents a video vehicle tracking algorithm based on polar Fourier descriptor according to the Fourier transform properties. This paper first gets the ideal vehicle for the target object and extract its contour by using background subtraction for the reason that the traffic monitoring video scenes are fixed and then gets the video frequency spectrum characteristics of the vehicle by implementing Discrete Fourier Transform to the image data in polar coordinates and then achieves the video vehicle tracking based on linear prediction and shape similarity measurement. Experimental results show that this algorithm's tracking accuracy is higher and time-consuming is less.
机译:智能交通领域中基于视频的车辆跟踪运动是一个重要的研究问题。根据傅立叶变换特性,提出了一种基于极傅立叶描述子的视频车辆跟踪算法。由于交通监控视频场景是固定的,本文首先得到了目标对象的理想车辆,并通过背景减法提取了它的轮廓,然后通过对图像数据进行离散傅里叶变换获得了车辆的视频频谱特性。在极坐标中,然后基于线性预测和形状相似性测量来实现视频车辆跟踪。实验结果表明,该算法的跟踪精度较高,耗时较少。

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