Head segmentation and head orientation in 3D space for pose estimation of multiple people

We present an algorithm for establishing head orientations of multiple persons in 3D space. Using multiple features from grayscale images (i.e., binary blobs, silhouette contours, and intensity distributions), our algorithm achieves foreground separation, head segmentation, and head-orientation classification, respectively. The information is then combined to form an integrated representation about how the heads of multiple persons are configured in 3D space in order to describe their relative position. The algorithm classifies each head orientation, ranging from 0 to 360 degrees of rotation on a horizontal plane, into eight classes by using a moment-based method. The algorithm can be easily extended to video sequences of image frames for describing how head poses change over time in relation to each person involved in a scene. Experimental results are presented and illustrated.