In this paper, we propose a robust real-time head tracking algorithm using a pan-tilt-zoom camera. In the algorithm, the shape of the head is assumed as an ellipse and a model color histogram is acquired in advance. Then, in the first frame, a user defines the position and scale of a head. In the following frame, we consider the ellipse selected in the previous frame as the initial position, and apply the mean-shift procedure to move the position to the target center where the color histogram similarity to the reference one is maximized. Here, the reference color histogram is adaptively updated from the model color histogram by using the one in the previous frame. Then, by using gradient information, the position and scale of the ellipse are further refined. Large background motion often makes the initial position not converge to the target position. To alleviate this problem, we estimate a reliable initial position by compensating the background motion. Here, to reduce the computational burden of motion estimation, we use the vertical and horizontal 1-D projection dataset. Extensive experiments show that a head is well tracked even when a person moves fast and the scale of the head changes drastically.