Computer Science ›› 2017, Vol. 44 ›› Issue (Z11): 199-201, 232.doi: 10.11896/j.issn.1002-137X.2017.11A.041

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HOG Pedestrian Detection Algorithm of Multiple Convolution Feature Fusion

GAO Qi-yu and FANG Hu-sheng   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Pedestrian detection is utilized as the fundamental of various computer vision applications.A typical and effective solution of pedestrian detection is combining histogram of oriented gradient (HOG) with support vector machine (SVM).In this paper,we proposed a novel pedestrian detection method,which uses convolutional neural network (CNN) in HOG+SVM to obtain more comprehensively feature description through a variety of convolutional kernels in CNN.Firstly,it extracts shallow features from data set by CNN,and uses CNN’s characteristics of displacement,and scale and deformation invariance.Then,it merges strongly relevant features by analyzing correlation coefficient of shallow features.After this,it extracts HOG features from CNN shallow features which are weakly correlated with each other.Finally,it uses SVM to complete the training and classification.The experimental results show that the proposed method can obtain higher accuracy than the existing method in pedestrian detection.

Key words: Pedestrian detection,Convolutional neural network,Multi-convolutional features,Histogram of oriented gradient

[1] PEARSON K.On Lines and Planes of Closest Fit to Systemsof Points in Space [J].Philosophical Magazine,1901,2(6):559-572.
[2] KIRBY M,SIROVICH L.Application of the KL Procedure for the Characterization of Human Faces [J].IEEE Trans.Pattern Analysis and Machine Intelligence,1990,12(1):103-108.
[3] TURK M,PENTLAND A.Eigenfaces for Recognition[J].Jour-nal of Cognitive Neuroscience,1991,3(1):71-86.
[4] TURK M,PENTLAND A.Face recognition using eigenfaces[C]∥Proc.IEEE Conf.on Computer Vision and Pattern Recognition.1991:586-591.
[5] 甘玲,邹宽中,刘肖.基于PCA降维的多特征级联的行人检测[J].计算机科学,2016,3(6):308-311.
[6] YANG J,ZHANG D,FRANGI A F,et al.Two-DimensionalPCA:A New Approach to Appearance-Based Face Representation and Recognition [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):131-137.
[7] 张焕龙,郑卫东,舒云星,等.基于区域协方差矩阵和2DPCA学习的视频跟踪方法研究[J].计算机科学,2015,2(9):278-281.
[8] WANG L,WANG X,ZHANG X,et al.The equivalence of two-dimensional PCA to line-based PCA[J].Pattern Recognition Letter,2005,6(1):57-60.
[9] GAO Q,ZHANG L,ZHANG D,et al.Comments on On Image Matrix Based Feature Extraction Algorithms [J].IEEE Trans.on Syst.Man & Cybern.B Cybern.,2007,7(5):194-197.
[10] 张贤达.矩阵分析与应用[M].清华大学出版社,2004:54-57.
[11] SIM T,BAKER S,BSAT M.The cmu pose,illumination,andexpression (pie) database[C]∥Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition.2002.
[12] KANADE T,COHN J F,TIAN Y L.Comprehensive database for facial expression analysis[C]∥Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition.2000:46-53.
[13] LUCEY P,CHON J F,KANADE T,et al.The extended cohn-kanade dataset (ck+):A complete expression dataset for action unit and emotion-specified expression[C]∥Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis.2010:94-101.

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