计算机科学 ›› 2014, Vol. 41 ›› Issue (9): 320-324.doi: 10.11896/j.issn.1002-137X.2014.09.062

• 图形图像与模式识别 • 上一篇    

一种改进HOG特征的行人检测算法

田仙仙,鲍泓,徐成   

  1. 北京联合大学信息服务工程实验室 北京100101;北京联合大学信息服务工程实验室 北京100101;北京联合大学信息服务工程实验室 北京100101
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目:高密度动态人群场景的多源图像融合研究(61271370),北京市教委科技项目(CIT&TCD20130513),北京市教育委员会科技计划面上项目(SQKM201411417010)资助

Improved HOG Algorithm of Pedestrian Detection

TIAN Xian-xian,BAO Hong and XU Cheng   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对HOG特征检测准确率高、计算量大的特点,通过对HOG特征的结构进行调整,提出了使用Fisher特征挑选准则来挑选出有区别能力的行人特征块,得到MultiHOG特征。该算法结合线性SVM二值分类器,实现行人滑动窗口检测。用Inria标准数据集和自行拍摄数据集进行了测试,结果证明该算法较HOG在准确率及实时性上都有很大的提高。

关键词: HOG特征,Fisher 特征挑选,MultiHOG特征,SVM分类

Abstract: In view of the HOG with large amount of calculation and the high detection accuracy,through the structural adjustment of the HOG features,the paper put forward that using Fisher feature selection criterion picks out the feature blocks full of ability to distinguish characteristics of pedestrian,and finally produces MultiHOG characteristics.Combined with the Lib-SVM classifier, the algorithm in the paper detects pedestrians with slided windows,and has a higher accuracy and real-time performance than HOG.

Key words: HOG features ,Fisher feature selection,MultiHOG characteristic,SVM classification

[1] Geronimo D,Lopez A.Survey of pedestrain detection for ada-vanced driver assistance systems[J].IEEE Trans.On Pattern Analysis and Machine Intelligence,2010,2(7):1239-1258
[2] Luo R C,Chen O.Wireless and Pyroelectric Sensory Fusion System for Indoor Human/Robot Localization and Monitoring[J].IEEE/ASME Transactions on Mechatronics,2013,18(3):845-853
[3] Uddin M-Z,Kim D-H,Kim J T,et al.An Indoor Human Activity Recognition System for Smart Home Using Local Binary Pattern Features with Hidden Markov Models[J].Indoor and Built Environment,2013,22(1):289-298
[4] Dalai N,Tfiggs B.Histograms of Oriented Gradients for Hu-manDetection[C]∥Proceedings of IEEE Computer Society Conference On Computer Vision and Pattern Recognition.IEEE Press,2005:886-893
[5] Ding Jian-hao,Wang Yi-gang,Geng Wei-dong.An HOG-CT human detector with histogram-based search[J].Multimedia Tools and Applications,2013,63(3):791-807
[6] Dohi K,Negi K,Shibata Y,et al.FPGA Implementation of Human Detection by HOG Features with AdaBoost[J].IEICE Transactionson Information and Systems,2013,96(8):1676-1684
[7] Cristina C,Daniela M,De Diego M,et al.HoGG:Gabor andHoG-based human detection for surveillance in non-controlled environments[J].Neurocomputing,2013,100:19-30
[8] Walk S.New Features and Insights for Pedestrian Detection[C]∥2010 IEEE Conference on Computer Vision and Pattern Recognition.2010:1030-1037
[9] Zeng Cheng-bin,Ma Hua-dong.Robust Head-Shoulder Detec-tion by PCA-Based Multilevel HOG-LBP Detector for People Counting[C]∥Proceedings of the 2010 20th International Conference on Pattern Recognition.2010:2069-2072
[10] Wojek C,Schiele B.A performance evaluation of single andmulti-featrue people detection[C]∥Proc.DAGMJ.2008
[11] Ojala T,Pietikainen M.A comparetive study of texture meas-ures with classification based on feature distributions[J].Pattern Recognition,1996,19(3):51-59
[12] Mu Y,Yan S,Liu Y.Discriminative local binary patterns for pedestrain detection in personal album[C]∥Proc.IEEE CVPR.2008
[13] MIT Database.http://db.csail.mit.edu/.MIT-CBCL Pedestrian Database
[14] INRIA Database.http://pascal.inrialpes.fr/data/human/
[15] CASIA GAIT.http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!