Performance characterization of image stabilization algorithms

  • Balakirsky S
  • Chellappa R
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Abstract

This paper compares three image stabilization algorithms when used as preprocessors for a target tracking application. These algorithms vary in computational complexity, accuracy, and ability. Algorithm 1 is capable of only pixel-level realignment of imagery, while Algorithms 2 and 3 are capable of full subpixel stabilization with respect to translation, rotation, and scale. The algorithms are evaluated on their performance in the stabilization of one synthetic forward looking infrared (FLIR) data set and two real {FLIR} imagery data sets. The evaluation tools incorporated include mean absolute error of the output data set and the overall performance of an automatic target acquisition system (developed at the Army Research Laboratory) that uses the algorithms as a front end preprocessor. We found that for this tracking application, extremely accurate subpixel stabilization was a requirement for proper operation. We also found that in this application, Algorithm 3 performed significantly better than the other two algorithms.

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Authors

  • S.B. Balakirsky

  • R. Chellappa

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