Segmentation and Tracking Using Adaptive Color Segmentation
                                    
   This project proposes a real-time tracking and segmention for
single-colored objects using stastistical color model and its adaptation .
Because of presence of similar color parts and motion in the background,
and changes in lighting conditions, the feature extraction task becomes
difficult. To provide reliable feature extraction, I proposes a system
that fuses motion segmentation and color segmentation, and updates color
distribution parameters for every frame.  The feature extraction process
is based on color and motion information and accompanied by predictive
Kalman filtering process. This enables the system to achieve reliable and
real time performance in complex environments.