Human segmentation on sports simulator using hierarchical template mathcing

This paper presents human segmentation using parts of human body shape, which is for recognizing human pose on sports simulator. It is very difficult to segment human body in unsettled environment which includes various clothes style, illumination or the shape of human body. Principle feature points are extracted in seven parts of the human body: head, shoulder, elbow, hand, hip, knee, and foot. Five parts of human body such as head, hand, hip, knee and foot segmented using hierarchical template matching and shape average image. The other parts of human body segmented using shadow edge and geometric information because shoulder and elbow boundary lines are ambiguous so these inner parts need to delicate processing. To evaluate proposed algorithm, 200 pose images for 5 human on ETRI database have used and we have acquired an encouraging experimental result of 85 percent on average.