Histogram of Oriented Gradients Feature Extraction Without Normalization

Ling Zhang, Weihong Zhou, Jingwei Li, Juan Li, Xin Lou
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引用次数: 5

Abstract

In this paper, the effects of normalization in the histogram of oriented gradients (HOG) are studied and a HOG feature extraction pipeline without normalization is proposed. In the proposed pipeline, the functionality of normalization is merged into the gradient generation step by replacing the original linear difference based gradients with logarithmic gradients. Due to the discrete property of the pixel values, the logarithmic operation can be easily implemented using a lookup table (LUT) with a depth of 2N, where N is the bit-width of the pixels. Theoretical analysis and experimental results show that the proposed normalization-free HOG feature based logarithmic gradient is close to the original version and can be used in the pedestrian detection algorithms without performance degradation. It is shown in the experiments that by skipping the time-consuming normalization step, the processing speed of HOG feature extraction can be significantly improved.
不归一化的定向梯度直方图特征提取
研究了归一化对定向梯度直方图(HOG)的影响,提出了一种无需归一化的HOG特征提取管道。在该管道中,通过用对数梯度代替原来的基于线性差分的梯度,将归一化功能合并到梯度生成步骤中。由于像素值的离散性,可以使用深度为2N的查找表(LUT)轻松实现对数操作,其中N是像素的位宽。理论分析和实验结果表明,本文提出的无归一化HOG特征的对数梯度算法接近原始算法,可用于行人检测算法,且性能不下降。实验表明,通过跳过耗时的归一化步骤,可以显著提高HOG特征提取的处理速度。
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