A Novel Data Association Algorithm Based on Fuzzy Logic for Visual Object Tracking

Abstract

In this paper, we propose a novel fuzzy logic data association algorithm to resolve the problem of visual multi-object tracking. First of all, a fuzzy logic data association approach is proposed to calculate the association probabilities between the objects and observations (or detection responses) by use of a set of fuzzy if-then rules. Based on the similarities of object appearance, shape and motion models, several fuzzy inference systems are designed to confirm the fuzzy membership degrees between the objects and observations, the association probability is generated by weighted affinities, where the weight of the affinity is the output of fuzzy logic. Finally, the experiment results on several public sequences demonstrate that the proposed algorithm has superiorities over other comparatively leading tracking algorithms in terms of accuracy and robustness.