The tracking and pose estimation of people in video made challenging problem due to the variability
of human appearance. In this thesis, we research on articulated 3D human motion and pose estimation.
However the human posture recognition has many dicult problems to be solved, because the human
body model is deformable object, besides we have to process the complexity of natural scenes and the
high dimensionality of articulated body models. Also the several problems such as texture of clothes,
lighting change, illumination, various colors made the problem harder.
Recent approaches to estimating and tracking human posture exploit articulated human body models
in which the body is viewed as a kinematic tree. Since the particle filter has been proven as very successful
algorithm for non-linear and non-gaussian estimation problem, the particle filtering was alternative
method for human body tracking. Our goal is to develop a modified particle filter which is shown to
be effective at searching the high-dimensional configuration spaces (40 dimensions) by using time series
prediction. Our results was suggested in constrained laboratory environments and showed our methods
perform quite well than others. Multiple cameras and background subtraction, however, are required to
achieve reliable tracking performance. In this thesis, we proposed the admix vector and time series vector
(TSV) particle filter. The proposed likelihood which is admix vector can reduce the computation time
and solve the asymmetric problem at the same time. To solve the problem of the robust tracking of fast
moving object, the time series prediction was employed to predict states following regression information
of RBF neural network.