Statistical moments based methods for detecting sub-pixel target tracks in large image sequences

Christoph C. Borel; David J. Bunker; Lori A. Mahoney

doi:10.1117/12.2050004

19 June 2014 Statistical moments based methods for detecting sub-pixel target tracks in large image sequences

Christoph C. Borel, David J. Bunker, Lori A. Mahoney

Proceedings Volume 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II; 90890U (2014) https://doi.org/10.1117/12.2050004
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

This paper reviews and compares the performance of several methods to detect target tracks in image sequences. The targets are assumed to be sub-pixel or not resolved by the imaging system, and moving over a static background. To process the resulting large amount of data requires simple, fast and robust processing methods to quickly find and display tracks of moving targets in a single image. An object moving through a pixel in a scene will momentarily perturb the pixel intensity signal, introducing a change of both skewness and kurtosis in the intensity histogram relative to an undisturbed pixel. Numerical experiments show that for Gaussian and Poisson distributed system noise higher order moments (<2) perform better than second order detectors.

Citation Download Citation

Christoph C. Borel, David J. Bunker, and Lori A. Mahoney "Statistical moments based methods for detecting sub-pixel target tracks in large image sequences", Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 90890U (19 June 2014); https://doi.org/10.1117/12.2050004

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