Optical flow optimization using parallel genetic algorithm

Olmo Zavala-Romero; Guillermo Botella; Anke Meyer-Bäse; Uwe Meyer Base

doi:10.1117/12.883680

3 June 2011 Optical flow optimization using parallel genetic algorithm

Olmo Zavala-Romero, Guillermo Botella, Anke Meyer-Bäse, Uwe Meyer Base

Proceedings Volume 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX; 80581E (2011) https://doi.org/10.1117/12.883680
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

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Olmo Zavala-Romero, Guillermo Botella, Anke Meyer-Bäse, and Uwe Meyer Base "Optical flow optimization using parallel genetic algorithm", Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581E (3 June 2011); https://doi.org/10.1117/12.883680

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KEYWORDS

Optical flow

Composites

Genetic algorithms

Error analysis

Video

Biomimetics

Convolution

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