Abstract
We investigate a radial basis function neural network for the control of a video camera for tracking a target moving at high speed. The adjusting of the weights and parameters of the network minimizes the performance measureP that is the sum of the squared errors between the target position on the screen and the center of the screen over an entire trajectory of the target. Every adjustment of a single weight requires the evaluation of the performance measure over the fixed target trajectory. A very general target trajectory is required for generalized training of the network to appropriately control any camera and target situation. The results show considerable promise for this and similar cases where a trainable controller is desired.
Similar content being viewed by others
References
Aizerman MA, Braverman EM, Rozonoer LI (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Contr 25: 821–837
Bashkirov OA, Braverman EM, Muchnik IB (1964) Potential function algorithms for pattern recognition learning machines. Autom Remote Contr 25: 629–631
Sprecht DF (1968) A practical technique for estimating general regression surfaces. LMSC-6-79-68-6, Lockheed Missile and Space Co., Palo Alto, CA
Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Systems 2: 321–355
Moody JE, Darken CJ (1988) Fast learning in networks of locallytuned processing units. Neural Computation 1: 281–294
Sugisaka M (1995) Neural network control for recognition and tracking of moving objects. Proceedings of the ISCA Fourth Golden West International Conference on Intelligent Systems, San Francisco, USA, pp 189–193
Looney CG (1997) Pattern recognition using neural networks. Oxford University Press. New York
Looney CG (1996) Advances in feedforward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowledge and Data Eng 8.2: 211–217
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Looney, C.G. A self-organizing video neuro-tracker. Artificial Life and Robotics 1, 73–78 (1997). https://doi.org/10.1007/BF02471118
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02471118