Fuzzy tracking of multiple objects
LEONID I. PERLOVSKY · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Existing tracking algorithms have difficulties with multiple objects in heavy clutter'. As a number of clutter objects increases, it is becoming increasingly difficult to maintain and especially to initiate tracks. A near optimal algorithm, the Multiple Hypothesis Tracking (MHT)2, initiates tracks by considering all possible associations between multiple objects and clutter event on multiple frames. This, however, requires combinatorially large amount of computation, which is difficult to handle even for neural networks, when a number of clutter objects is large. A partial solution to this problem is offered by the Joint Probability Density Association (JPDA) tracking algorithm3, which performs fuzzy associations of objects and tracks, eliminating combinatorial search. However, the JPDA algorithm performs associations only on the last frame using established tracks and is, therefore, unsuitable for track initiation. The problem is becoming even more complicated for imaging, incoherent sensors, when direct measurement of object velocity via the Doppler effect is unavailable. We have applied a previously developed MLANS neural network'5'6 to the problem of tracking multiple objects in heavy clutter. In our approach the MLANS performs a fuzzy classification of all objects in multiple frames into multiple classes of txacks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: the MLANS tracking combines advantages of both the JPDA and the MHT, it is capable of track initiation by considering multiple frames, and it eliminates combinatorial search via fuzzy associations.