Title :
Gait recognition based on embedded Hidden Markov models
Author_Institution :
Sch. of Mech. Eng., Shenyang Ligong Univ., Shenyang, China
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;
Conference_Titel :
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location :
Jilin
Print_ISBN :
978-1-61284-719-1
DOI :
10.1109/MEC.2011.6025746
