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Proceedings Paper

Real-time target tracking system based on joint transform correlator and neural network algorithm
Author(s): Eun-Soo Kim; Sang-Yi Yi; Jin-Ho Lee
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Paper Abstract

In this paper, we present a new opto-neural approach to the problem of multi-target tracking. The proposed hybrid opto-neural system uses an optical joint transform correlator to reduce the massive input target data into a few correlation peak signals and then a massive parallel computational neural network algorithm is used for effective target tracking data association based on these correlation signals. For real-time operation, a nonlinear joint transform correlator is optically implemented using a high resolution LCD spatial light modulator (SLM) and a new track based on field (TBF) neural network tracking algorithm is introduced to tackle the effective multi-target data association in a real-time basis. Through the computer simulation, the performance of the proposed hybrid opto-neural tracking system is evaluated and some experimental results on simultaneous tracking of multi-targets are also provided.

Paper Details

Date Published: 30 October 1992
PDF: 12 pages
Proc. SPIE 1812, Optical Computing and Neural Networks, (30 October 1992); doi: 10.1117/12.131208
Show Author Affiliations
Eun-Soo Kim, Kwangwoon Univ. (South Korea)
Sang-Yi Yi, Kwangwoon Univ. (South Korea)
Jin-Ho Lee, Kwangwoon Univ. (South Korea)


Published in SPIE Proceedings Vol. 1812:
Optical Computing and Neural Networks
Ken Yuh Hsu; Hua-Kuang Liu, Editor(s)

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