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Proceedings Paper

Differential geometry measures of nonlinearity for the video filtering problem
Author(s): Mahendra Mallick; Barbara F. La Scala
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Paper Abstract

Video cameras onboard multiple unmanned aerial vehicles (UAVs) can provide effective and inexpensive tracking and surveillance functions for ground targets. In our previous work, we quantified the degree of nonlinearity (DoN) of the video filtering problem by considering the perspective transformation for the video measurement model and constant velocity motion for the target dynamic model. In this paper, we generalize the formulation by using a more realistic video measurement model which is based on the perspective transformation, radial and tangential lens distortions, scale, offset, and skew. The centroid pixel coordinates of a target in the digital image represent the sensor measurement for this model. This measurement model is commonly used in photogrammetry, computer vision, and video tracking, where significant height variation can occur. Since the measurement model is a nonlinear function of the target state, the filtering problem is nonlinear. We quantify the DoN of the video filtering problem by calculating the differential geometry based parameter-effects curvature and intrinsic curvature. These measures help a filter designer to select an appropriate nonlinear filtering algorithm for the video filtering problem so that tracking accuracy and computational load requirements are satisfied. Our results show that the DoN of the video filtering problem is quite low and hence a computationally simple filter such as the extended Kalman filter (EKF) is a better choice than the particle filter (PF) which has a much higher computational cost. The state estimation accuracies of the EKF and PF are nearly the same.

Paper Details

Date Published: 16 April 2008
PDF: 13 pages
Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 69690M (16 April 2008); doi: 10.1117/12.781537
Show Author Affiliations
Mahendra Mallick, Science Applications International Corp. (United States)
Barbara F. La Scala, Univ. of Melbourne (Australia)

Published in SPIE Proceedings Vol. 6969:
Signal and Data Processing of Small Targets 2008
Oliver E. Drummond, Editor(s)

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