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.

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

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