Fuzzy tracking of multiple objects

Abstract

The authors have applied a previously developed MLANS neural network to the problem of tracking multiple objects in heavy clutter. In their approach the MLANS performs a fuzzy classification of all objects in multiple frames in multiple classes of tracks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: the MILANS tracking combines advantages of both the JPD and the MHT, it is capable of track initiation by considering multiple frames, and it eliminates combinatorial search via fuzzy associations.<>