Human gait recognition by pyramid of HOG feature on silhouette images

Guang Yang, Yafeng Yin, Jeanrok Park, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationOptical Pattern Recognition XXIV
DOIs
StatePublished - 2013
EventOptical Pattern Recognition XXIV - Baltimore, MD, United States
Duration: 29 Apr 201330 Apr 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8748
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceOptical Pattern Recognition XXIV
Country/TerritoryUnited States
CityBaltimore, MD
Period29/04/1330/04/13

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