VEHICLE RECOGNITION WITH LOCAL-FEATURE BASED ALGORITHM USING PARALLEL VISION BOARD

VEHICLE RECOGNITION WITH LOCAL-FEATURE BASED ALGORITHM USING PARALLEL VISION BOARD
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使用并行视觉板的基于局部特征的算法进行车辆识别

DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
K. Ikeuchi
K. Ikeuchi
中科院分区：
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文献类型：
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作者：
T. Yoshida;M. Kagesawa;T. Tomonaka;K. Ikeuchi

文献摘要

被引文献

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本文描述了一种鲁棒的车辆识别方法。作者的系统是基于局部特征配置的，他们已经证明它在红外图像中工作得很好。该算法是基于他们之前的工作，这是一个推广的特征窗方法。该方法具有以下三个优点：(1)即使部分车辆被遮挡，也能进行检测。(2)即使车辆因冲出车道而发生平移，也能进行检测。(3)不需要从输入图像中分割车辆区域。的确，他们首先使用红外图像开发了他们的系统，但他们的系统并不一定要使用红外图像。本文将该系统应用于超广角图像，并进行了室外实验，证明了该系统对光学图像的有效处理。他们的系统擅长检测车辆的位置，因此它不仅对车辆检测有用，而且对电子收费（ETC）、专用短程通信（DSRC）等应用也很有用，该系统需要知道它与哪辆车通信。
This paper describes a robust method for recognizing vehicles. The authors' system is based on local-feature configuration, and they have already shown that it works very well in infrared images. The algorithm is based on their previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) it can detect even if part of vehicles is occluded. (2) It can detect even if vehicles are translated due to running out of the lanes. (3) It does not require them to segment vehicle areas from input images. It is true that they have first developed their system with infrared images, but it is not essential for their system to employ infrared images. In this paper, applying their system on images of super wide-angle, they have shown that their system is effective to optical images, performing an outdoor experiment. Their system is good at detecting locations of vehicles, hence it will be useful for not only vehicle detection but also such application, electronic toll collection (ETC), dedicated short-range communications (DSRC) or so, that system needs to know which vehicle it communicates with.