Model-based tracking of moving object

This paper describes a real-time tracking system which detects an object entering the field of view of a camera and executes tracking of the detected object by controlling a servo device in such a way that a target always lies at the center of an image frame. In order to detect and track a moving object, we basically apply model matching strategy. We allow the model of a target to vary dynamically during the tracking process so that it can assimilate variations of shape and intensities of a target. We also utilize a Kalman filter to encode a tracking history into state parameters of the filter. The estimated state parameters will then be used to reduce search areas for model matching and to control a servo device. Experimental results show that model adaptation allows robust tracking of a target object in dynamic environments. These experiments also confirm that the predicted values of a Kalman filter are very accurate in controlling a servo device and finding out the search areas for model matching. This paper concludes with theoretical bounds within which a tracking system can follow the movement of a target object.