Abstract. We propose a method for tracking human heads, where interaction between hypotheses plays a key role. We model appearances of the human head and generate hypotheses for a human head in the image in the model space. We then propagate and reform hypotheses over time in turn to realize tracking human heads. During tracking, we bring about interaction between hypotheses to eliminate the hypotheses denoting false positives and, at the same time, to maintain the hypotheses denoting human heads.
We propose a method for detecting and tracking a human head in real time from image sequence. The proposed method has three advantages. 1) We employ a fixedviewpoint pan-tilt-zoom camera to acquire image sequence. 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 with 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.
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