We present an object tracking system based on an edge model for the target characterization. The target position is estimated by looking for the model in the current image using a Hausdorff partial distance. Target is searched only in a sub-window of current edge image. Its boundaries are determined by Kalman filter estimation that uses target dynamics to predict the current position. We use a spiral searching strategy to find the actual position. The target model is updated in each iteration by using unidirectional partial distance from the image to the model. This model is refined by an enclosure operator in order to perform the target/background discrimination. The parameters of our system can be modified in an active way along the tracking task. The system is shown to be robust to illumination changes and pose variations. The system has been also embedded in a mobile robot for personal robotics applications and integrated in a real-time OS (3 Hz).
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