Development of gazing algorithms for tracking-oriented recognition

Sharon X. Wang; Gary Chen; Demetrios Sapounas; Hongchi Shi; Richard Peer

doi:10.1117/12.277142

23 June 1997 Development of gazing algorithms for tracking-oriented recognition

Sharon X. Wang, Gary Chen, Demetrios Sapounas, Hongchi Shi, Richard Peer

Proceedings Volume 3069, Automatic Target Recognition VII; (1997) https://doi.org/10.1117/12.277142
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Sharon X. Wang, Gary Chen, Demetrios Sapounas, Hongchi Shi, and Richard Peer "Development of gazing algorithms for tracking-oriented recognition", Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); https://doi.org/10.1117/12.277142

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