Locally adaptive combining colour and depth for human body contour tracking using level set method

In this study, the authors present a novel human body contour tracking method which adaptively combines colour and depth cues of RGB-D images in level set framework. The authors model the body object by active contour. When the body object is far away from the background objects, it is relatively easy to separate the object from the background using the depth cue. In this case, the depth cue should dominate the evolution of the active contour. When some part of the body is very close to the background objects, the discriminability of the depth decreases rapidly. In this local region the colour cue should dominate the evolution, whereas the depth cue should still play an important role in other regions. To achieve these objectives, the authors propose to use a superpixel-based locally adaptive weight map to determine the importance of the depth cue. Moreover, to obtain more accurate contours and to avoid error drifting, based on the two novel properties of the human body surface in the depth images, the authors propose two simple but effective algorithms to refine the tracking results of the level set method. The promising results demonstrate the performance of the proposed method.