Hybrid Feature and Template Based Tracking for Augmented Reality Application

Visual tracking is the core technology that enables the vision-based 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 template-based 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.