Hybrid Feature and Template Based Tracking for Augmented Reality Application

Visual tracking is the core technology that enables the visionbased augmented reality application. Recent contributions in visual tracking are dominated by template-based tracking approaches such as ESM due to its accuracy in estimating the camera pose. However, it is shown that the template-based tracking approach is less robust against large inter-frames displacements and image variations than the feature-based tracking. Therefore, we propose to combine the feature-based and template-based tracking into a hybrid tracking model to improve the overall tracking performance. The feature-based tracking is performed prior to the template-based tracking. The feature-based tracking estimates pose changes between frames using the tracked feature-points. The templatebased tracking is then used to refine the estimated pose. As a result, the hybrid tracking approach is robust against large inter-frames displacements and image variations. It also accurately estimates the camera pose. Furthermore, we will show that the pose adjustment performed by the feature-based tracking reduces the number of iterations necessary for the ESM to refine the estimated pose.