Fast-Coding Robust Motion Estimation Model in a GPU



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García Sánchez, Carlos and Botella Juan, Guillermo and Sande, Francisco de and Prieto Matías, Manuel (2015) Fast-Coding Robust Motion Estimation Model in a GPU. In SPIE 9400, Real-Time Image and Video Processing, Febrero 2015, San Francisco, California, United States.

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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.

Item Type:Conference or Workshop Item (Lecture)
Uncontrolled Keywords:Motion Estimation, GPU, OpenACC
Subjects:Sciences > Computer science
Sciences > Computer science > Hardware
ID Code:31457
Deposited On:16 Jul 2015 07:56
Last Modified:16 Jul 2015 11:59

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