Optimal object association from pairwise evidential mass functions

Object association is often a prior step in the data fusion process, especially for multiple objects tracking and multisensor data fusion. The approach introduced in this paper associates objects detected in a scene by two sensors, while modeling uncertainty using the Dempster-Shafer theory of belief functions. Sensor information is transformed into pairwise mass functions, which are combined using Dempster´s rule of combination. The result of this combination allows us to find the most plausible relation between two sets of objects by solving a linear programming problem. Experimental results with real data acquired from sensors embedded in intelligent vehicles are presented.