Improving reliability of people tracking by adding semantic reasoning

Abstract

Even the best performing object tracking algorithm on well known datasets, commits several errors that prevent a concrete adoption in real case scenarios unless you do not accept some compromise about tracking quality and reliability. The aim of this paper is to demonstrate that adding to a traditional object tracking solution a knowledge based reasoner build on top of semantic web technologies, it is possible to identify and properly manage common tracking problems. The proposed approach has been evaluated using View 001 and View 003 of the PETS2009 dataset with interesting results.