Abstract : In this work, a real-time image-based camera tracker is designed for live television production studios. The major concern is to decrease camera tracking expenses by an affordable vision-based approach. First, a dense keyframe model of the static studio scene is generated using image-based dense tracking and bundle adjustment. Online camera tracking is then defined as registration problem between the current RGB-D measurement and the nearest keyframe. With accurate keyframe poses, our camera tracking becomes virtually driftless. The static model is also used to avoid moving actors in the scene. Processing dense RGB-D measurements requires special attention when aiming for real-time performance at 30 Hz. We derive a real-time tracker from our cost function for a low-end GPU. The system requires merely a RGB-D sensor, laptop and a low-end GPU. Camera tracking properties are compared with KinectFusion. Our solution demonstrates robust and driftless real-time camera tracking in a television production studio environment.