Human body parts tracking using pictorial structures and a genetic algorithm

Tracking people and localising body parts is a challenging computer vision problem because people move unpredictably under circumstances of partial and full occlusions. In this work we focus on the problem of automatic detection and tracking of humans and we propose a combined background subtraction (BS) /foreground modeling and a matching technique based on a genetic algorithm. The developed architecture combines a self-adaptive cluster level BS scheme using a Gaussian mixture model (GMM) and an appearance learning model of the foreground with pictorial structures. The model of the human body parts is then matched with the background subtracted sequence using an efficient genetic algorithm. The efficiency of the designed technique is demonstrated over real video sequences.