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DOI:
计算机系统应用英文版:2013,22(7):104-107
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基于HOG特征和机器学习的工程车辆检测
(1.中国科学院研究生院, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.南京师范大学 地理科学学院, 南京 210023)
Vehicles Detection Based on Histograms of Oriented Gradients and Machine Learning
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.College of Geographical Science, Nanjing Nornal University, Nanjing 210023, China)
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Received:December 13, 2012    Revised:January 07, 2013
中文摘要: 针对智能监控在露天矿区中的应用, 提出了一种在大场景、远距离、多角度的环境下自动识别工程车辆的算法. 该算法利用梯度方向直方图特征(HOG)作为图像描述, 并结合支持向量机(SVM)训练分类器的方法, 实现图像中工程车辆的检测. 为提高检测效率和准确度, 采用Hough变换直线检测的方法提取包含目标的区域. 实验表明, 算法能够在较大分辨率的复杂图像中识别出检测目标, 有较高的准确度, 对于实验中不同的环境具有较强的鲁棒性.
Abstract:We propose a vehicle detection algorithm for Intelligent Surveillance used in open pit area . The algorithm detects vehicle in the image using Histograms of Oriented Gradients features (HOG) as the image description, combined with support vector machine (SVM) classifier training. In order to improve the detection efficiency and accuracy, Hough transform line detection method to extract the region that contains the target to reduce the search range. The experiments show that the algorithm can be able to recognition the target in complex images, with high degree of accuracy, robustness for the different environments of the experiment.
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张全发,蒲宝明,李天然,孙宏国.基于HOG特征和机器学习的工程车辆检测.计算机系统应用,2013,22(7):104-107
ZHANG Quan-Fa,PU Bao-Ming,LI Tian-Ran,SUN Hong-Guo.Vehicles Detection Based on Histograms of Oriented Gradients and Machine Learning.COMPUTER SYSTEMS APPLICATIONS,2013,22(7):104-107