Human Tracking System.

This paper presents a comprehensive framework for tracking coarse human model from sequences of synchronized monocular grayscale images in single or multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis from sequences ofsynchronized monocular grayscale images in different camera Coordinates and other existing techniques in motion detection, segmentation, and patter recognition. This human tracking is an important task in many vision applications. The main steps in video analysis are two: detection of interesting moving objects and tracking of such objects from frame to frame. In a similar vein, most tracking algorithms use pre-speci ed methods for preprocessing. There are several objects tracking algorithms i.e. Meanshift, Camshift, Kalman lter with different preprocessing methods. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking can be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image spatially and temporally, using Bayesian Classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features.