Human head tracking using adaptive appearance models with a fixed-viewpoint pan-tilt-zoom camera

We propose a method for detecting and tracking a human head in real time from an image sequence. The proposed method has three advantages: (1) we employ a fixed-viewpoint pan-tilt-zoom camera to acquire image sequences; with the camera, we eliminate the variations in the head appearance due to camera rotations with respect to the viewpoint; (2) we prepare a variety of contour models of the head appearances and relate them to the camera parameters; this allows us to adaptively select the model to deal with the variations in the head appearance due to human activities; (3) we use the model parameters obtained by detecting the head in the previous image to estimate those to be fitted in the current image; this estimation facilitates computational time for the head detection. Accordingly, the accuracy of the detection and required computational time are both improved and, at the same time, the robust head detection and tracking are realized in almost real time. Experimental results in the real situation show the effectiveness of our method.