Computer Vision-Based Human Body Segmentation and Posture Estimation

Abstract

This paper proposes a new method for vision-based human body posture estimation using body silhouette and skin-color information. A moving object segmentation algorithm is first proposed to distinguish the human body from the background using a sequence of images. This algorithm uses a fast Euler number computation technique to automatically determine the threshold of both frame and background differences. After segmentation, a sequence of image processing approaches then creates a complete silhouette of the human body. The objective of posture estimation is to locate five significant body points, including the head, tips of the feet, and tips of the hands. These significant points are first selected from convex points on a defined distance curve. A number of heuristic rules based on body shape characteristics are used to select the proper points among these convex candidates. These rules use features like the principal and minor axes of the human body, their interactions with the silhouette contour, the relative distances between convex points, and the curvature of convex points. An auxiliary skin-color feature is used when the silhouette shape features alone are not sufficient to estimate the significant points. Experimental results show that the proposed approach can efficiently and effectively locate the significant body points for most postures.