An Evidential Pattern Matching Approach for Vehicle Identification

Abstract

In this paper, we propose a novel pattern matching approach for vehicle identification based on belief functions. Distances are computed within a belief decision space rather than directly in the feature space as traditionally done. The main goal of the paper is to compare performances obtained when using several distances between belief functions recently introduced by the authors. Belief functions are modeled using the outputs of a set of modality-based 1-NN classifiers, two distinct uncertainty modeling techniques and are combined with Dempster’s rule. Results are obtained on real data gathered from sensor nodes with 4 signal modalities and for 4 classes of vehicles (pedestrian, bicycle, car, truck). Main results show the importance of the uncertainty technique used and the interest of the proposed pattern matching approach in terms of performance and expressiveness.