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Proceedings Paper

Complex motion measurement using genetic algorithm
Author(s): Jianjun Shen; Dan Tu; Zhenkang Shen
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Paper Abstract

Genetic algorithm (GA) is an optimization technique that provides an untraditional approach to deal with many nonlinear, complicated problems. The notion of motion measurement using genetic algorithm arises from the fact that the motion measurement is virtually an optimization process based on some criterions. In the paper, we propose a complex motion measurement method using genetic algorithm based on block-matching criterion. The following three problems are mainly discussed and solved in the paper: (1) apply an adaptive method to modify the control parameters of GA that are critical to itself, and offer an elitism strategy at the same time (2) derive an evaluate function of motion measurement for GA based on block-matching technique (3) employ hill-climbing (HC) method hybridly to assist GA's search for the global optimal solution. Some other related problems are also discussed. At the end of paper, experiments result is listed. We employ six motion parameters for measurement in our experiments. Experiments result shows that the performance of our GA is good. The GA can find the object motion accurately and rapidly.

Paper Details

Date Published: 12 December 1997
PDF: 8 pages
Proc. SPIE 3173, Ultrahigh- and High-Speed Photography and Image-based Motion Measurement, (12 December 1997); doi: 10.1117/12.294526
Show Author Affiliations
Jianjun Shen, National Univ. of Defense Technology (China)
Dan Tu, National Univ. of Defense Technology (China)
Zhenkang Shen, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 3173:
Ultrahigh- and High-Speed Photography and Image-based Motion Measurement
C. Bruce Johnson; Andrew Davidhazy; James S. Walton; Takeharu Goji Etoh; C. Bruce Johnson; Donald R. Snyder; James S. Walton, Editor(s)

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