Online clustering of high-dimensional trajectories under concept drift
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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We propose an Online Inferential Framework (OIF) for tracking humans and objects under occlusions with Kalman tracker. The OIF is constructed on knowledge representation schemes, precisely semantic logic where each node represents the detected moving object and flow paths represent the association among the moving objects. A maximum likelihood is computed using our CWHI-based technique and Bhattacharyya coefficient. The proposed framework efficiently interprets multiple possibilities of tracking by manipulating the "propositional logic" on the basis of maximum likelihood at a time window. The logical propositions are built by formularizing facts, semantic rules and integrity constraints associated with tracking. The experimental results show that our novel OIF is able to track objects along with the interpretation of their physical states accurately and reliably under complete occlusion, illustrating its contribution and advantages over various other approaches.