A Neural Networks Based Visual Tracking System
 
A. Boni, A. Dolce, S. Rovetta, and R. Zunino 
DIBE -- University of Genova 

In: Proceedings of the
1996 International Workshop on Neural Networks for Identification, Control,
Robotics, and Signal/Image Processing (NICROSP '96) 

Copyright (c) 1996 Institute of Electrical and Electronics Engineers, Inc.
All rights reserved.



A B S T R A C T 
---------------
A visual tracking system based on a neural architecture is presented. The approach to
target identification is non--conventional in that it relies on an architecture
composed of standard neural networks (multi--layer perceptrons), and exploits the
information contained in simple features extracted from the images, using a small number
of operations. Therefore the tracking functions are learned by examples, rather than
implemented directly. The training set is composed of various geometrical shapes,
in various sizes, with and without a background, pre--processed to yield the data
vectors. The system exploits a two--level neural networks hierarchy with a number
of parallel networks and an ``arbiter.'' The selected hardware implementation
is based on a cartesian arm and a Motorola VMEexec workstation, that hosts the system
but does not take part in the actual computation. This allows a true real--time operation.