Visual Hand Tracking on Depth Image using 2-D Matched Filter

Hand detection has been the central attention of human-machine interaction in recent researches. In order to track hand accurately, traditional methods mostly involve using machine learning and other available libraries, which requires a lot of computational resource on data collection and processing. This paper presents a method of hand detection and tracking using depth image which can be conveniently and manageably applied in practice without the huge data analysis. This method is based on the two-dimensional matched filter in image processing to precisely locate the hand position through several underlying codes, cooperated with a Delta robot. Compared with other approaches, this method is comprehensible and time-saving, especially for single specific gesture detection and tracking. Additionally, it is friendly-programmed and can be used on variable platforms such as MATLAB and Python. The experiments show that this method can do fast hand tracking and improve accuracy by selecting the proper hand template and can be directly used in the applications of human-machine interaction. In order to evaluate the performance of gesture tracking, a recorded video on depth image model is used to test theoretical design, and a delta parallel robot is used to follow the moving hand by the proposed algorithm, which demonstrates the feasibility in practice.