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Proceedings Paper

Development of gazing algorithms for tracking-oriented recognition
Author(s): Sharon X. Wang; Gary Chen; Demetrios Sapounas; Hongchi Shi; Richard Peer
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Paper Abstract

This paper describes the development of a computer vision system that hosts a suite of gazing algorithms for tracking oriented recognition (GATOR). The goal of the GATOR system is to accomplish robust automatic target recognition and tracking (ATRT) tasks at a video rate of 30 Hz. The uniqueness of GATOR is that it employs tightly structured, multiple, advanced ATR algorithms to progressively increase the confidence level during recognition, which enables a rugged performance and real-time processing in complicated battlefields. The biologically inspired GATOR system consists of three advanced image understanding algorithms: (1) a novel target wavelet filter to facilitate image registration, motion segmentation, and target tracking; (2) a morphological neural network (MNN) to provide target recognition and target list updating; and (3) a fuzzy logic data fusion scheme to integrate recognition results from multiple frames of images. In GATOR, these algorithms are optimally integrated at different stages of recognition and tracking, which maximally enhances the strengths of each algorithm. Initial testing of the individual algorithms has demonstrated the potential of the GATOR for battlefield applications.

Paper Details

Date Published: 23 June 1997
PDF: 12 pages
Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); doi: 10.1117/12.277142
Show Author Affiliations
Sharon X. Wang, ID Vision, Inc. (United States)
Gary Chen, ID Vision, Inc. (United States)
Demetrios Sapounas, Naval Surface Warfare Ctr. (United States)
Hongchi Shi, Univ. of Missouri/Columbia (United States)
Richard Peer, McDonnell Douglas Aerospace (United States)

Published in SPIE Proceedings Vol. 3069:
Automatic Target Recognition VII
Firooz A. Sadjadi, Editor(s)

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