TY - GEN
T1 - Human gait recognition by pyramid of HOG feature on silhouette images
AU - Yang, Guang
AU - Yin, Yafeng
AU - Park, Jeanrok
AU - Man, Hong
PY - 2013
Y1 - 2013
N2 - As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.
AB - As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.
UR - http://www.scopus.com/inward/record.url?scp=84881164528&partnerID=8YFLogxK
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U2 - 10.1117/12.2015981
DO - 10.1117/12.2015981
M3 - Conference contribution
AN - SCOPUS:84881164528
SN - 9780819495396
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Optical Pattern Recognition XXIV
T2 - Optical Pattern Recognition XXIV
Y2 - 29 April 2013 through 30 April 2013
ER -