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Proceedings Paper

Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing
Author(s): James S. J. Lee; Dziem D. Nguyen; C. Lin
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Paper Abstract

A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

Paper Details

Date Published: 27 March 1989
PDF: 8 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960289
Show Author Affiliations
James S. J. Lee, Boeing Electronics High Technology Center (United States)
Dziem D. Nguyen, Boeing Electronics High Technology Center (United States)
C. Lin, Boeing Electronics High Technology Center (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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