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In this paper, a novel fuzzy logic data association algorithm is proposed for online visual multi-object tracking. Firstly, in the proposed algorithm, in order to incorporate expert experience into the data association for the improvement of performance in multi-object tracking, a fuzzy inference system based on knowledge is designed by using a set of fuzzy if-then rules. Given the error and change of error of motion, shape and appearance models in the last prediction, these rules are used to determine the fuzzy membership degrees that can be used to substitute the association probabilities between the objects and the measurements (or detection responses). Secondly, in order to deal with the fragmented trajectories caused by long-term occlusions, a track-to-track association approach based on the fuzzy synthetic function is proposed, which can effectively stitch track fragments (tracklets). Because of this, the proposed algorithm has the advantage that it does require no assumption of statistical models of measurement noise and of object dynamics. The experiment results on several public data sets show the efficiency and the ability to minimize the number of fragment tracks of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a new intuitionistic fuzzy joint probabilistic data association filter for multitarget tracking in a cluttered environment. In the proposed algorithm, the joint association probabilities in JPDAF are reconstructed by utilizing intuitionistic fuzzy membership degrees of the measurements belonging to the targets. To compute the intuitionistic fuzzy membership degree, a new intuitionistic fuzzy clustering method is proposed based on intuitionistic fuzzy point operator, which can extract useful information from uncertainty information of measurement. At the same time, two new weight assignments are introduced to deal with the uncertainty of measurement, which lead to two different data association methods, IF-JPDAF1 and IF-JPDAF2. Moreover, according to the characteristic of multitarget tracking, a new intuitionistic index of intuitionistic fuzzy set is defined. Finally, experiment results show the proposed algorithms have advantages over the conventional methods (including the JPDAF, Fitzgerald's JPDAF and MEF-JPDAF) in terms of efficiency and robustness. [ABSTRACT FROM AUTHOR]