Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking

In this paper, a genetic tracker with adaptive neuro-fuzzy inference system (GT-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the genetic algorithm (GA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the GT-ANFIS, the joint probabilistic data association filter (JPDAF), the genetic tracker (GT), and the genetic tracker with neural network (GT-NN) are compared with each other for six different tracking scenarios. It was shown that the tracks estimated by using proposed GT-ANFIS agree better with the true tracks than the tracks predicted by the JPDAF, the GT, and the GT-NN.

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