机器学习方法在汽车图像检测和分类中的应用

IF 0.6 Q3 Engineering
B. Ashwini, B. N. Yuvaraju
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引用次数: 1

摘要

基于直方图方向梯度(HOG)特征的支持向量机(SVM)是许多视觉感知职责的真正标准。由于光照和场景复杂性的变化,移动车辆检测已成为非常重要的组成部分之一。因此,本文提出了一种HOG特征描述符。HOG特征对光照变化不敏感,并且在表征物体形状和外观方面表现更好。通过组合所有HOG特征来构建特征向量,这些特征是训练用于车辆分类的线性SVM分类器所必需的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of machine learning approach in detection and classification of cars of an image
Support vector machine (SVM) qualified on histogram orientation gradients (HOG) features is a genuine standard across many visual awareness responsibilities. Due to the change in the illumination and scene complexity, moving vehicle detection has become one of the very important components. Therefore, in this paper, a HOG feature descriptor is proposed. HOG features are not perceptive to illumination change and performance is better in characterising object shape and appearance. A feature vector is built by combining all the HOG features, which are required to train a linear SVM classifier for classification of vehicles.
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CiteScore
2.10
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