A Multiprocessor-Oriented Visual Tracking System
Stefano Rovetta and Rodolfo Zunino

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 46, NO. 4, AUGUST 1999
Pages 842-850

Abstract—

The design and prototypal realization of a visual tracking system is presented.
The approach to target identification is nonconventional, in that it relies
on an architecture composed of multiple standard neural networks (multilayer
perceptrons) and exploits the information contained in simple features extracted
from images, performing a small number of operations. Therefore, the tracking
functions are learned by examples, rather than implemented directly. The system
demonstrates that a quite complex task such as visual target tracking can be
easily obtained by a suitable neural architecture. The fast tracking algorithm
and the parallel structure allow a true real-time operation. The system exploits
a two-level neural-network hierarchy with a number of parallel networks and an
"arbiter." The training set consists of various geometrical shapes, preprocessed
to yield the data vectors. The experimental hardware implementation is based on
multiple processing units, implementing the neural architecture, and serves as a
prototype for the analysis of the system in practice. A small-sized realization
can also be obtained.

Index Terms—

Machine vision, multilayer perceptrons, multi-processing, neural network applications,
neurocontrollers, tracking.