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Proceedings Paper

Predictive optical-flow algorithm for aircraft detection
Author(s): Jeffrey W. McCandless; Albert J. Ahumada
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Paper Abstract

A computer vision algorithm was developed to detect moving aircraft located in video images. Using a gradient-based approach, the algorithm computes optical flow vectors in each frame of the sequence. Vectors with similar characteristics (location, magnitude, and direction) are clustered together using a spatial consistency test. Vectors that pass the spatial consistency test are extended temporally to make predictions about the optical flow locations, magnitudes, and directions in subsequent frames. The actual optical flow vectors that are consistent with the predicted vectors are labeled as vectors associated with a moving target. The algorithm was tested on images obtained with a video camera mounted below the nose of a Boeing 737. The algorithm correctly detected an aircraft from a distance of one mile in over 80% of the frames with zero false alarms.

Paper Details

Date Published: 6 October 1998
PDF: 11 pages
Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); doi: 10.1117/12.325764
Show Author Affiliations
Jeffrey W. McCandless, NASA Ames Research Ctr. (United States)
Albert J. Ahumada, NASA Ames Research Ctr. (United States)


Published in SPIE Proceedings Vol. 3522:
Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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