In this paper we present a visual object tracking approach to extract motion information for Augmented Reality (AR) systems. Our proposed system tracks the target object by applying a model based pose estimationalgorithm. The approach is to fuse information from an active contours model and optical flow motion estimation.The optical flow is used to provide a constraint on the deformable model motion and place the initialcontour in the region of interest of the active contour. For pose estimation we use the Extended Kalman Filter(EKF), the measurement equation models the feature points of object in image and the process model predictsthe behavior of the system based on the current state and estimates the position and orientation of the objecttoward the camera coordinate frame. The algorithm is tested in real time and shows to be robust and efficient.