Real-Time Robust Tracking with Commodity RGBD Camera

Commodity RGBD cameras such as Kinect sensor have recently proven a large success in many indoor robotics and computer vision applications. Nevertheless, tracking and motion estimation algorithms cannot rely on Kinect raw outputs because of their low accuracy. These consumer cameras can only produce precise depth measures within a close range. However, they do suffer from potential noises when the target is further away from permitted. This paper proposes an innovative adaptation of Kalman filtering scheme to improve the accuracy of Kinect as a real-time tracking device. We present a detailed proof of Kalman filter adaptation on Kinect data, and we demonstrate the robustness of our approach on a real dataset.