RT Conference Proceedings
T1 Fast-Coding Robust Motion Estimation Model in a GPU
A1 García Sánchez, Carlos
A1 Botella Juan, Guillermo
A1 Sande, Francisco de
A1 Prieto Matías, Manuel
AB 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 asaccelerators 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 performancerates 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.
YR 2015
FD 2015-02-10
LK https://hdl.handle.net/20.500.14352/24978
UL https://hdl.handle.net/20.500.14352/24978
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
DS Docta Complutense
RD 17 ago 2024