DocumentCode
3394904
Title
Gait recognition based on embedded Hidden Markov models
Author
Qi Yang
Author_Institution
Sch. of Mech. Eng., Shenyang Ligong Univ., Shenyang, China
fYear
2011
fDate
19-22 Aug. 2011
Firstpage
1457
Lastpage
1460
Abstract
In this paper, a gait silhouette or feature cycle is divided into several temporally adjacent clusters. Each cluster is calculated and gets several adjacent PFDEI binary images. Embedded Hidden Markov mode (EHMM) is built to describe the relationship among the PFDEI. In every PFDEI state we divided several substate to describe the PFDEI image. Frame difference image is good chosen to describe substate. Sparse representation is applied to analyze binary image to learning its parameters.
Keywords
embedded systems; gait analysis; hidden Markov models; image motion analysis; image representation; EHMM; PFDEI image; embedded hidden Markov models; feature cycle; gait recognition; gait silhouette; sparse representation; Conferences; Decision support systems; Erbium; Handheld computers; Mechatronics; EHMM; PFDEI; dictionary; sparse representation; veterbi;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location
Jilin
Print_ISBN
978-1-61284-719-1
Type
conf
DOI
10.1109/MEC.2011.6025746
Filename
6025746
Link To Document