In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.
[1] P.F.Felzenswalb and D.P.Huttenlocher, "Efficient matching of pictorial
structure" in Proceeding of the computer vision and pattern
Recognition, 2000, pp. 66-73.
[2] K.Rohr, "Towards model based recognition of human movements in
image sequence" in CVGIP, 1994, vol.59
[3] Y.Song, X.Feng and P.perona, "Towards detection of human motion".
in Proceeding of the computer vision and pattern Recognition, 2000
[4] D.M.Gavrila and L.Davis, "Towards 3-D model-based Tracking and
recognition of Human Movement: a Multi-view Approach" In IEEE
international conference on automatic Face and Gesture
Recognition.1995
[5] S.Niyogi and E.Adelson, "Analyzing and recognizing walking figures in
XYT", in Proceeding of the computer vision and pattern Recognition.
1994, pp. 469-474.
[6] Robert.T.Collins, Ralph Gross and Jianbo shi, "Silhouette-based Human
Identification from Body Shape and Gait" In IEEE international
conference on automatic Face and Gesture Recognition 2002
[7] R.Rosales and S.Sclaroff, "Inferring Body Pose without Tracking Body
Parts". in Proceeding of the computer vision and pattern Recognition,
2000.
[8] C.Andrieu, N.De.Freitas,A.Doucet and M.jordan, "An Introduction to
MCMC for Machine Learning". Machine Learning, 50, 5-43, 2003
[9] W.K.Hasting, "Monte Carlo Sampling Methods Using Markov Chain
and Their Application". Biometrika, 1970.
[10] T. Zhao and R. Nevatia. "Bayesian Human Segmentation in Crowded
Situations", in Proceeding of the computer vision and pattern
Recognition, pp.459-466. 2003
[11] W.Gilk, S.Richardson and D.Spiegelhalter, Markov Chain Monte Carlo
in Practice Chapman and Hall, 1996
[12] B.Walsh, Markov Chain Monte Carlo and Gibbs sampling. Lecture
Notes for EEB 596z, 2002.
[13] M. Hason "Tutorial on Markov Chain Monte Carlo" Technical report,
Los Alamos National laboratory, 2000.
[1] P.F.Felzenswalb and D.P.Huttenlocher, "Efficient matching of pictorial
structure" in Proceeding of the computer vision and pattern
Recognition, 2000, pp. 66-73.
[2] K.Rohr, "Towards model based recognition of human movements in
image sequence" in CVGIP, 1994, vol.59
[3] Y.Song, X.Feng and P.perona, "Towards detection of human motion".
in Proceeding of the computer vision and pattern Recognition, 2000
[4] D.M.Gavrila and L.Davis, "Towards 3-D model-based Tracking and
recognition of Human Movement: a Multi-view Approach" In IEEE
international conference on automatic Face and Gesture
Recognition.1995
[5] S.Niyogi and E.Adelson, "Analyzing and recognizing walking figures in
XYT", in Proceeding of the computer vision and pattern Recognition.
1994, pp. 469-474.
[6] Robert.T.Collins, Ralph Gross and Jianbo shi, "Silhouette-based Human
Identification from Body Shape and Gait" In IEEE international
conference on automatic Face and Gesture Recognition 2002
[7] R.Rosales and S.Sclaroff, "Inferring Body Pose without Tracking Body
Parts". in Proceeding of the computer vision and pattern Recognition,
2000.
[8] C.Andrieu, N.De.Freitas,A.Doucet and M.jordan, "An Introduction to
MCMC for Machine Learning". Machine Learning, 50, 5-43, 2003
[9] W.K.Hasting, "Monte Carlo Sampling Methods Using Markov Chain
and Their Application". Biometrika, 1970.
[10] T. Zhao and R. Nevatia. "Bayesian Human Segmentation in Crowded
Situations", in Proceeding of the computer vision and pattern
Recognition, pp.459-466. 2003
[11] W.Gilk, S.Richardson and D.Spiegelhalter, Markov Chain Monte Carlo
in Practice Chapman and Hall, 1996
[12] B.Walsh, Markov Chain Monte Carlo and Gibbs sampling. Lecture
Notes for EEB 596z, 2002.
[13] M. Hason "Tutorial on Markov Chain Monte Carlo" Technical report,
Los Alamos National laboratory, 2000.
@article{"International Journal of Information, Control and Computer Sciences:56270", author = "Rui. Zhang and Yiming. Pi", title = "Human Body Configuration using Bayesian Model", abstract = "In this paper we present a novel approach for human
Body configuration based on the Silhouette. We propose to address
this problem under the Bayesian framework. We use an effective
Model based MCMC (Markov Chain Monte Carlo) method to solve
the configuration problem, in which the best configuration could be
defined as MAP (maximize a posteriori probability) in Bayesian
model. This model based MCMC utilizes the human body model to
drive the MCMC sampling from the solution space. It converses the
original high dimension space into a restricted sub-space constructed
by the human model and uses a hybrid sampling algorithm. We
choose an explicit human model and carefully select the likelihood
functions to represent the best configuration solution. The
experiments show that this method could get an accurate
configuration and timesaving for different human from multi-views.", keywords = "Bayesian framework, MCMC, model based, human
body configuration.", volume = "2", number = "7", pages = "2412-6", }