3D object pose estimation plays a crucial role in computer and robotic vision. In this thesis, a novel model-based object pose estimation algorithm is proposed by integrating template matching and PnP pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Using the matched keypoints, a 2D mapping between the reference image and the observed object can be formulated by a homography matrix, which is used as an initial setting for a template-based tracking algorithm. Based on the visual tracking result, the corresponding points between image keypoints and control points of the CAD model of the object can be determined efficiently. Finally, the 3D pose of the object with respect to the camera is estimated by adopting the PnP algorithm based on the corresponding points between 2D image keypoints and 3D model control points. The experimental results validate the estimation accuracy and real-time performance of the proposed model-based object pose estimation algorithm.