Computer Science ›› 2016, Vol. 43 ›› Issue (9): 295-300.doi: 10.11896/j.issn.1002-137X.2016.09.059

Previous Articles     Next Articles

Gait Recognition Based on Decomposition of Optical Flow Components

LUO Zheng-ping, LIU Yan-jun and YANG Tian-qi   

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

Abstract: Gait recognition has gained tremendous attention for its characteristics of long distance and hard-to-disguise.Aiming at the problem of insufficient information of the existing feature extraction method,this paper proposed a novel gait recognition method based on the decomposition of optical flow components.The positive transverse or longitudinal components in gait optical flow image are decomposed by rows and columns,then the transverse and longitudinal components of optical flow for each row or colum are calculated,and four feature vectors are obtained.The four vectors are fused according to their weight in recognition.Principle components analysis and linear discriminant analysis techniques-are combined,and dynamic time warping algorithm is used to match.Finally,K-nearest neighbor algorithm is used for classification.Experiments on CASIA Database B and C show that,the proposed method achieves recognition accuracy of 97%,90% and 64% respectively under the conditions of normal,backpack-wearing and coat-wearing,88% and 87% under conditions of slow walking and fast walking.

Key words: Gait recognition,Optical flow,Principle components analysis,Linear discriminant analysis,Dynamic time warping,Feature fusion

[1] Lee T K M,Belkhatir M,Sanei S.A comprehensive review of past and present vision-based techniques for gait recognition[J].Multimed Tools Appl,2014,2(3):2833-2869
[2] Zhang Er-hu,Zhao Yong-wei,Xiong Wei.Active energy image plus 2DLPP for gait recognition[J].Signal Processing,2010,0(7):2295-2302
[3] Bouchrika I,Nixon M S.Model-based feature extraction for gait analysis and recognition[J].Lect Notes Comput Sc,2007,8:150-160
[4] Tafazzoli F,Safabakhsh R.Model-based human gait recognition using leg and arm movements[J].Eng Appl Artif Intel,2010,3(8):1237-1246
[5] Chen C H,Liang J M,Zhao H,et al.Frame difference energy image for gait recognition with incomplete silhouettes[J].Pattern Recognition Letters,2009,0(11):977-984
[6] Muramatsu D,Shiraishi A,Makihara Y,et al.Gait-Based Person Recognition Using Arbitrary View Transformation Model[J].IEEE Transactions on Image Processing,2015,4(1):140-154
[7] Chen X,Yang T Q,Xu J M.Cross-view gait recognition based on human walking trajectory[J].J Vis Commun Image R,2014,5(8):1842-1855
[8] Horn B,Schunck B.Determining Optical Flow[J].Artificial Intelligence,1981,7(2):185-203
[9] Lam T H W,Cheung K H,Liu J N K.Gait flow image:A silhouette-based gait representation for human identification[J].Pattern Recognition,2011,4(4):973-987
[10] Yu C C,Cheng C H,Fan K C.A Gait Classification Systemusing Optical Flow Features[J].J Inf Sci Eng,2014,0(1):179-193
[11] Yang Ya-zhou,Tu Dan,Li Guo-hui.Gait recognition using flow histogram energy image[C]∥22nd International Conference on Pattern Recognition.2014:444-449
[12] Pers J,Sulic V,Kristan M,et al.Histograms of optical flow for efficient representation of body motion[J].Pattern Recognition Letters,2010,1(11):1369-1376
[13] Yang Yang,Guo Ji-chang.Gait recognition method based onspatial distribution of optical flow[J].Application Research of Computers,2013,0(7):2206-2209(in Chinese) 杨阳,郭继昌.基于光流空间分布的步态识别方法[J].计算机应用研究,2013,0(7):2206-2209
[14] Xu Yan-qun,Zhang Bin.Application of Segmentation Based on Optical Flow in Gait Recognition[J].Computer Science,2012,9(4):275-277,2(in Chinese) 徐艳群,张斌.一种基于光流的多区域分割在步态识别中的应用[J].计算机科学,2012,9(4):275-277,2
[15] Lucas Bruce D,Takeo K.An Iterative Image Registration Technique with an Application to Stereo Vision[C]∥Procedings of Imaging Understanding Workshop.1981:121-130
[16] Harold H.Analysis of a complex of statistical variables intoprincipal components[M].Baltimore:Warwick & York,1933
[17] Wang Cheng-liang,Lan Li-bin,Zhang Yu-wei,et al.Face recognition based on principle component analysis and support vector machine[C]∥2011 3rd International Workshop on Intelligent Systems and Applications.2011
[18] Zhou C J,Wang L,Zhang Q,et al.Face recognition based on PCA image reconstruction and LDA[J].Optik,2013,4(22):5599-5603
[19] Balakrishnama S,Ganapathiraju A,Picone J.Linear Discrimi-nant Analysis for signal processing problems[C]∥IEEE Southeastcon ’99.1999:78-81
[20] Hernandez-Vela A,Bautista M A,Perez-Sala X,et al.Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D[J].Pattern Recognition Letters,2014,0:112-121
[21] Sheikhan M,Gharavian D,Ashoftedel F.Using DTW neural-based MFCC warping to improve emotional speech recognition[J].Neural Comput Appl,2012,1(7):1765-1773
[22] Chinese Academy of Sciences.Institute of Automation Gait Database.
[23] Liu Zhi-yong, Feng Guo-can, Chen Wei-fu.Gait RecognitionBased on Local Binary Pattern and Discriminant Common Vector[J].Computer Science,2013,0(9):262-265(in Chinese) 刘志勇,冯国灿,陈伟福.基于局部二值模式和辨识共同向量的步态识别[J].计算机科学,2013,0(9):262-265
[24] Zeng W, Wang C.Gait recognition across different walkingspeeds via deterministic learning[J].Neurocomputing,2015,2:139-150

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .