Patch-based Gaussian mixture model for scene motion detection in the presence of atmospheric optical turbulence

Richard L. Van Hook; Russell C. Hardie

doi:10.1117/12.2558318

24 April 2020 Patch-based Gaussian mixture model for scene motion detection in the presence of atmospheric optical turbulence

Richard L. Van Hook, Russell C. Hardie

Author Affiliations +

Proceedings Volume 11394, Automatic Target Recognition XXX; 1139414 (2020) https://doi.org/10.1117/12.2558318
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

In long-range imaging regimes, atmospheric turbulence degrades image quality. In addition to blurring, the turbulence causes geometric distortion effects that introduce apparent motion in acquired video. This is problematic for image processing tasks, including image enhancement and restoration (e.g., superresolution) and aided target recognition (e.g., vehicle trackers). To mitigate these warping effects from turbulence, it is necessary to distinguish between actual in-scene motion and apparent motion caused by atmospheric turbulence. Previously, the current authors generated a synthetic video by injecting moving objects into a static scene and then applying a well-validated anisoplanatic atmospheric optical turbulence simulator. With known per-pixel truth of all moving objects, a per-pixel Gaussian mixture model (GMM) was developed as a baseline technique. In this paper, the baseline technique has been modified to improve performance while decreasing computational complexity. Additionally, the technique is extended to patches such that spatial correlations are captured, which results in further performance improvement.

Citation Download Citation

Richard L. Van Hook and Russell C. Hardie "Patch-based Gaussian mixture model for scene motion detection in the presence of atmospheric optical turbulence", Proc. SPIE 11394, Automatic Target Recognition XXX, 1139414 (24 April 2020); https://doi.org/10.1117/12.2558318

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