A NEURAL NETWORK ARCHITECTURE

FOR DETECTING MOVING OBJECTS. II

Valerio Cimagalli

Facolta' d'Ingegneria - Univ. di Roma "La Sapienzall Via Eudossiana 18, 00184 Roma

ITALY

S U M M A R Y

In the last few years, results obtained in the field of neural networks have grown at a quite exponential rate. Also the investigation of their capabilities and of their mechanisms has progressed significantly (see e.g. [ 13, [ 21 ) . Nevertheless the main property almost always used is the principle of the content addressable memory [ 3 ] , i.e. the capability of a neural net to construct in its phase space a set of attractor basins each corresponding to a learned pattern. From a little more extensive point of view, we may say that generally a neural net is used as a device that evolves to an equilibrium point starting from a given initial condition. This is true also in the case when the problem is not to classify or recognize a pattern, but to find the minimum in an optimization problem. Precisely in this way a neural solution was used in a recent paper by Sengupta and Iltis [ 4 ] for solving the multistage tracking data association problem.

A different and broader approach to the architecture and to the use of neural nets for solving the more general problem of processing time-varying inputs has been investigated since many months by a research team in Rome [ 5 ] , [ 6 ] . In a previous paper [7] this author proposed a completely new architecture, whose main characteristics may be summarized as follows:

a) The signal is processed in a spatio-temporal dimension. Time is not the independent variable in the solution of a set of differential equations as in the classical case, but it plays an essential role in the interaction on the time varying input and its processing. b) The purpose of the net is not, as usually, to classify and/or recognize patterns, nor to solve a problem of minimum energy, but to detect some characteristics of a signal varying with respect both to time and space.

- 125 -

input varying signal, mainly based on the unique role of coincidence detectors.

Such a network has been proved useful in solving the problem of detecting moving objects in a cluster.

In the present work the architecture of the net is outlined and its performance is discussed at some length together with its similarities and differences with respect to Cellular Neural Networks.

Results of computer simulations are showed and the problem of a hardware implementation is faced.

R E F E R E N C E S

[l] Grossberg S., IINonlinear neural networks: principles, mechanism and architecturestt, Neural Networks 1, 17-61 (1988).

[2] Hirsch M.W., IIConvergent Activation Dynamics in Continuous Time Networkst1, Neural Networks 2 , 331-349 (1989) .

[3] Kohonen T., Self-organization and Associative Memory (2nd Ed.), Berlin-Heidelberg-new York (1988).

[4] Sengupta D. and Iltis R.A., ItNeural Solution to the multitarget Tracking Data Association Problem", IEEE Trans. on Aerospace and Electronic Systems AES-25, 96-108 (1989).

[5] Cimagalli V., Giona M., Basti, G., Perrone A., Pasero E., "An Asymmetric Spin-Glass Model of Long-Term Memory in a Dynamic Network ArchitectureIt, Proc. IEEE-INNS

[6] Basti G., Perrone A., Cimagalli V., Giona M., Pasero E., Morgavi G., "A Dynamic Approach to Invariant Extraction from Time-Varying Inputs by Using Chaos in Neural Nets!!, to be published in: Proc. IEEE-INNS IJCNN- 90-San Diego-CA, (1990).

[7] Cimagalli V., "A Neural Network Architecture for Detecting Moving Objectst1, Proc. of the 3rd Italian Workshop on Parallel Architectures and Neural Networks (E. Caianiello Ed.), World Publishing, Singapore, 1990, in press.

IJCNN-90-WASH-DC 1, 333-336 (1990).