Depth image-based hand tracking in complex scene

Abstract

Object tracking via depth image sequences can output 3D motion trajectories, and is effective in solving many traditional problems such as illumination changes and projective distortion. However, the influence of complex scene in depth-based tracking has received little attention. Depth tracker is susceptible to many kinds of disturbances, such as nearby objects, moving distracters and noise, and these properties need to be considered in the design of a robust tracking algorithm. In this paper we investigated the depth image-based object tracking problem by a typical application: hand tracking. Firstly the properties of depth image and the disturbances are discussed. Secondly two different depth trackers are proposed to handle different scenarios: one is based on depth frame differencing, and the other one is based on the mean shift algorithm which is modified to utilize depth information. Finally, a robust fused tracker is proposed based on the two different trackers. This approach was tested on two groups of depth image sequences recorded by a Kinect device, and the experimental results show that the fused tracker is competent for accurate 3D hand tracking even under very complex scene.