GPU accelerated left/right hand-segmentation in first person vision

Wearable cameras allow users to record their daily activities from a user-centered (First Person Vision) perspective. Due to their favourable location, they frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications. Existent First Person Vision, methods understand the hands as a background/foreground segmentation problem that ignores two important issues: (i) Each pixel is sequentially classified creating a long processing queue, (ii) Hands are not a single “skin-like” moving element but a pair of interacting entities (left-right hand). This paper proposes a GPU-accelerated implementation of a left right-hand segmentation algorithm. The GPU implementation exploits the nature of the pixel-by-pixel classification strategy. The left-right identification is carried out by following a competitive likelihood test based the position and the angle of the segmented pixels.