Improvement of Feature Extraction Based on HOG

Zhe-Hao Liu
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引用次数: 1

Abstract

In recent decades, with the rapid development of science and technology, pedestrian detection has gradually begun to mature from the beginning. Pedestrian detection involves a number of disciplines and fields to achieve joint cooperation. As the basis of pedestrian detection, image processing needs to ensure the quality and speed of detection at the same time. Face recognition based on directional gradient histogram (HOG) has good accuracy in pedestrian detection. But at the same time, compared with other pedestrian detection feature extraction methods, the disadvantage of hog is that it takes too much time and can not guarantee the detection speed while improving the accuracy. On this premise, based on the idea of clustering, the hog features are clustered according to their gradient directions, and then the appropriate features are found out by statistical calculation to form the combined features, and the subsequent steps are carried out by the combined features. Through the experiment, without sacrificing the detection accuracy, the detection efficiency can be effectively improved by reducing the data dimension.
基于HOG的特征提取改进
近几十年来,随着科学技术的飞速发展,行人检测从一开始就逐渐开始走向成熟。行人检测涉及多个学科和领域实现联合合作。图像处理作为行人检测的基础,需要同时保证检测的质量和速度。基于方向梯度直方图(HOG)的人脸识别在行人检测中具有较好的准确性。但与此同时,与其他行人检测特征提取方法相比,hog的缺点是耗时太长,无法在提高精度的同时保证检测速度。在此前提下,基于聚类思想,根据hog特征的梯度方向对hog特征进行聚类,然后通过统计计算找出合适的特征组成组合特征,再由组合特征进行后续步骤。通过实验,在不牺牲检测精度的前提下,通过降低数据维数,可以有效地提高检测效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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