In this paper, a real-time head detection and tracking system called HeadFinder is proposed. HeadFinder is a robust system which detects heads of people appeared in video images and track them. For the sake of effective detection we pay attention to motion and shape of a head, both of which are robust features to noise in video images. Since what the moving circle is a head is almost always true in our life space, we utilized it to detect heads. First, we detect outline of moving people in difference images between two consecutive video frames. Next, for the sake of circle detection, we use Hough transform which is known as a robust shape detection method. After the position and size (radius) of the detected circle are registered as a head model, HeadFinder switches to tracking phase. In order to raise the efficiency of tracking, we predict the domain where head will move. The size of predicted domain is proportional to the reliability of the head model, that is, the number of times of pursuit successes by present. Performances of HeadFinder in indoor and outdoor environment, are examined. Through experiments, we confirmed that HeadFinder works robustly against environment change and works well in real-time by a simple hardware.
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