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In the man-machine interaction tracking and identification of individuals plays an important role. In this work, a framework for the service-robot Lisa, of the Active Vision Group, has been created to combine different methods for the detection, tracking and identification of individuals. First leg detection is performed to establish hypotheses for people using a 2D-laserscan. This assumption needs to be confirmed by an analysis of the Kinect point cloud. After successful confirmation online-boosting on RGB-data is performed for identification. The leg data will also be used with a linear Kalman filter to estimate the movement of people. Through the combination of of Kalman filter with leg detection and online-boosting people tracking should be enabled. Further receiving an interchange of persons should - by brief occlusion or faulty associate of legs - can be prevented.