Real-time both hands tracking using CAMshift with motion mask and probability reduction by motion prediction

Hand gesture interfaces are more intuitive and convenient than traditional interfaces. They are the most important parts in the relationship between users and devices. Hand tracking for hand gesture interfaces is an active area of research in image processing. However, previous works have limits such as requiring the use of multiple camera or sensor, working only with single color background, etc. This paper proposes a real-time both hands tracking algorithm based on “CAMshift (Continuous Adaptive Mean Shift Algorithm)” using only a single camera in multi-color backgrounds. In order to track hands robustly, the proposed algorithm uses “motion mask” to combine color and movement probability distributions and “probability reduction” for multi-hand tracking in non-limiting environments. Experimental results demonstrate that this algorithm can precisely track both hands of an operator in multi-color backgrounds and process the VGA size input sequences from a web camera in real time (about 25 fps).