改进的基于增强型HOG的行人检测算法  

Improved Pedestrian Detection Method Based on Enhanced HOG

作　　者：李文书[1] 韩洋 阮梦慧 王志骁 LI Wen-Shu;HAN Yang;RUAN Meng-Hui;WANG Zhi-Xiao(School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学信息学院,杭州310018

出　　处：《计算机系统应用》2020年第10期199-204,共6页Computer Systems & Applications

基　　金：国家重点研发计划重点专项(2018YFB1004901);国家自然科学基金(31771224,60702069);浙江省自然科学基金(LY17C090011)

摘　　要：行人检测在人工智能系统、车辆辅助驾驶系统和智能监控等领域具有重要的应用,是当前的研究热点.针对HOG特征不明显、支持向量机(SVM)分类器计算复杂度高,导致识别率低和检测速度慢的问题,本文提出了一种改进的基于增强型HOG的行人检测算法.该算法首先预处理原始图像并提取其HOG特征,然后增强该特征生成增强型HOG,经XGBoost分类器进行行人检测.在INRIA数据集上进行测试,实验结果表明所提算法识别率高达95.49%,有效地提高了行人检测性能.Pedestrian detection is a current research hotspot,which has important applications in the fields of artificial intelligence system,vehicle assistant driving system,and intelligent monitoring.In the process of pedestrian detection based on HOG feature,the HOG feature is not obvious,the SVM classifier has high computational complexity,resulting in low recognition rate and high missed detection rate,this study proposes an improved enhanced HOG feature combined with the eXtreme Gradient Boosting(XGBoost)classifier for pedestrian detection.Firstly,the original image is preprocessed to get saliency map and HOG features.Then,the contrast of HOG features is enhanced and the pedestrian detection analysis is carried out with XGBoost classifier.Tested with the INRIA dataset,the experimental results show that the proposed algorithm has a significant improvement in recognition rate and detection speed.

关 键 词：行人检测 HOG特征 增强型HOG XGBoost分类器 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]