Performance of the Duisburg Statistical Object Tracker on Test Data for PETS2000

This paper presents the first results obtained from the statistical object tracker developed at Duisburg University for the test data sequence provided by the organizers of the PETS2000 workshop. Our system differs from many other systems in that respect that it does not use any motion information at all for calculating the trajectory of a moving object in an image sequence, resulting in the fact that the proposed approach even makes the tracking of objects possible in the presence of background motions, for instance caused by other moving objects such as cars, or by camera operations as e.g. panning or zooming. Thus, the test sequence provided for PETS2000 represents a suitable testbed for our system, because it contains different moving objects and thus confronts the system with the challenge of tracking a specifically selected object in presence of other objects that are moving across the entire image and are even intersecting with the trajectories of other objects in the sequence. Our results confirm that our approach can handle the difficult task of tracking a specific person within the image sequence without losing its trace despite the previously mentioned problems caused by the other moving object in the sequence. The advantages of our tracking approach would have been even more apparent if the test sequence had been acquired with a moving camera, causing motion information distributed all over the entire image sequence. Of course, our system also has certain limitations. In the paper, we report on the advantages and limitations of our statistical object tracker.