Fast-coding robust motion estimation model in a GPU

Carlos García; Guillermo Botella; Francisco de Sande; Manuel Prieto-Matias

doi:10.1117/12.2077091

27 February 2015 Fast-coding robust motion estimation model in a GPU

Carlos García, Guillermo Botella, Francisco de Sande, Manuel Prieto-Matias

Proceedings Volume 9400, Real-Time Image and Video Processing 2015; 94000J (2015) https://doi.org/10.1117/12.2077091
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

Nowadays vision systems are used with countless purposes. Moreover, the motion estimation is a discipline that allow to extract relevant information as pattern segmentation, 3D structure or tracking objects. However, the real-time requirements in most applications has limited its consolidation, considering the adoption of high performance systems to meet response times. With the emergence of so-called highly parallel devices known as accelerators this gap has narrowed. Two extreme endpoints in the spectrum of most common accelerators are Field Programmable Gate Array (FPGA) and Graphics Processing Systems (GPU), which usually offer higher performance rates than general propose processors. Moreover, the use of GPUs as accelerators involves the efficient exploitation of any parallelism in the target application. This task is not easy because performance rates are affected by many aspects that programmers should overcome. In this paper, we evaluate OpenACC standard, a programming model with directives which favors porting any code to a GPU in the context of motion estimation application. The results confirm that this programming paradigm is suitable for this image processing applications achieving a very satisfactory acceleration in convolution based problems as in the well-known Lucas & Kanade method.

Citation Download Citation

Carlos García, Guillermo Botella, Francisco de Sande, and Manuel Prieto-Matias "Fast-coding robust motion estimation model in a GPU", Proc. SPIE 9400, Real-Time Image and Video Processing 2015, 94000J (27 February 2015); https://doi.org/10.1117/12.2077091

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KEYWORDS

Computer programming

Motion estimation

Optical flow

Convolution

Image filtering

Motion models

Algorithm development

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