“Real-time Both Hands Tracking Using Feature Point Gathering by KLT Tracker for Man-Machine Interface”

Intuitive man-machine interface based on a gesture with a touchpad device is becoming common. In the near future, the importance of gesture recognition using input from a video camera is expected to be high in order to widen applicable information terminals and their applications. Conventional works, however, use a complex input device combining plural cameras and sensors. Moreover, since most of their algorithms need high computational complexity and is good for images with a simple background, it's difficult to apply practical systems. This paper proposes a real-time single-input object tracking algorithm which can trace both hands precisely under a complex background. It makes it possible to attain both high accuracy and low complexity by applying a technique combining frame difference and color and decision of feature point gathering into the KLT (Kanade-Lucas-Tomasi) tracker, a kind of an optical flow. Software based evaluation results using a wide variety of test sequences (e.g. complex background and object shape change) show that the proposed algorithm achieves higher tracking accuracy compared with conventional ones. Furthermore, a processing performance is 13-16 frame per second, which means both hands can be tracked in real-time.