Robust object tracking based on RGB-D camera

A novel object tracking method based on RGB-D camera is proposed to handle fast appearance change, occlusion, background clutter which may arise for vision-based robot navigation. It makes use of appearance and depth information that are complementary to each other in visual perception to get robust tracking. First, RGB image and depth information are captured by the RGB-D camera. Then, an online updating appearance model is created with features extracted from RGB image. A motion model is created on plan-view map that is drawn from depth information and camera parameters. The estimation of object position and scale is performed on the motion model. Finally, appearance features are combined with position and scale information to track the target. The performance of our method is compared with a state-of-art video tracking method. It shows that our tracking method is more stable and accurate, and has overwhelming superiority when there is a great appearance change. A vision-based robot using our tracking method can navigate in cluttered environment successfully.