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HOG Pedestrian Detection Based on Edge Symmetry and Trilinear Interpolation

机译:基于边缘对称性和三线性插值的猪行人检测

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In computer vision, pedestrian detection is a key problem. In this paper, we propose to speed up the HOG+SVM algorithm without sacrificing the classification accuracy. In order to eliminate the effects of aliasing phenomenon that products in the process of HOG extraction, we used trilinear interpolation to extract feature. This paper proposed HOG pedestrian detection method based on edge symmetry. In these experiments, we used INRIA dataset. Traditional HOG pedestrian detection is presence of slow detection speed and low detection rate. Experiments show that using trilinear interpolation and edge symmetry not only can improve the detection effect, but also can improve the detection rate.
机译:在计算机视觉中,行人检测是一个关键问题。在本文中,我们建议加快HOG + SVM算法而不牺牲分类准确性。为了消除混叠现象的影响,该产品在猪提取过程中的产品,我们使用三线性插值来提取特征。本文提出了基于边缘对称的猪行人检测方法。在这些实验中,我们使用了Inria DataSet。传统的猪行人检测是检测速度慢的存在和低检测率。实验表明,使用三线圈插值和边缘对称性不仅可以提高检测效果,还可以提高检测率。

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