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Proceedings Paper

Detecting missile-like flying target from a distance in sequence images
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Paper Abstract

Automatic target detection (ATD) systems using imaging sensors have played a critical role in site monitoring, surveillance, and object tracking. Although numerous research efforts and systems have been designed to quickly detect and recognize missile-like flying targets in cluttered environments, detection of flying targets from a long distance and large format imagery data is still a challenge. The accuracy of target detection and recognition will greatly affect the performance of the target tracking system. In this paper, we propose a novel framework to quickly detect missile-like flying targets in a time-efficient manner. The framework is based on a coarse-to-fine strategy and consists of five components executed in a sequential order: (1) A rapid clustering operation performs fast image segmentation; (2) based on the segmentation results of three neighboring image frames, motion analysis identifies the regions of interest which contain the flying targets; (3) a specially-designed double-threshholding operator precisely segments the moving targets from the regions of interest; (4) a binary connectivity filter enhances the detected targets and removes the target noise; and (5) a contour method analyzes the boundary of the detected targets for verification. To test the proposed approach, a state-of-the-art 3D modeling and animation software tool was used to simulate target flight and attack. Experimental results, obtained from the electro-optical (EO) images generated from the 3D simulations, illustrate a wide variety of target and clutter variability, and demonstrate the effectiveness and robustness of the proposed approach.

Paper Details

Date Published: 17 April 2008
PDF: 10 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69680G (17 April 2008); doi: 10.1117/12.777463
Show Author Affiliations
Xiaokun Li, DCM Research Resources LLC (United States)
Genshe Chen, DCM Research Resources LLC (United States)
Erik Blasch, Air Force Research Lab. (United States)
Khanh Pham, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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