Tabu search for human pose recognition

Abstract

The use of computer vision techniques to build hands-free input devices has long been a topic of interest to researchers in the field of natural interaction. In recent years Microsoft's Kinect has brought these technologies to the layman, but the most commonly used libraries for Kinect human pose recognition are closed-source. There is not yet an accepted, effective open-source alternative upon which highly specific applications can be based. We propose a novel technique for extracting the appendage configurations of users from the Kinect camera's depth feed, based on stochastic local search techniques rather than per-pixel classification.