Optimal full motion video registration with rigorous error propagation

John Dolloff; Bryant Hottel; Peter Doucette; Henry Theiss; Glenn Jocher

doi:10.1117/12.2058715

19 June 2014 Optimal full motion video registration with rigorous error propagation

John Dolloff, Bryant Hottel, Peter Doucette, Henry Theiss, Glenn Jocher

Proceedings Volume 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II; 908909 (2014) https://doi.org/10.1117/12.2058715
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor’s trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to “down-stream” Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object’s 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

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John Dolloff, Bryant Hottel, Peter Doucette, Henry Theiss, and Glenn Jocher "Optimal full motion video registration with rigorous error propagation", Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 908909 (19 June 2014); https://doi.org/10.1117/12.2058715

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KEYWORDS

Sensors

Error analysis

Image registration

Image sensors

Video

Stochastic processes

Image processing

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