2006Unpublished venueRequires access

Performance of Optical Flow Techniques on Graphics Hardware

Marko Đurković, Michael E. Zwick, Florian Obermeier, Klaus J. Diepold

Open publisher page 11 citations

Abstract

Since graphics cards have become programmable the recent years, numerous computationally intensive algorithms have been implemented on the now called general purpose graphics processing units (GPGPUs). While the results show that GPGPUs regularly outperform CPU based implementations, the question arose how optical flow algorithms can be ported to graphics hardware. To answer the question, the optimal algorithm structure to maximize the performance gained by using graphics cards has to be found. In this paper we compare the performance of two algorithms that are highly different in structure, implemented on both CPU and graphics hardware. Analyzing the results of the CPU and GPGPU implementation, we explore the mapping of the algorithms to the graphics hardware and thereof extract information about a preferred structure of optical flow algorithms for GPGPU based implementation

About this research paper

What this paper is about

Since graphics cards have become programmable the recent years, numerous computationally intensive algorithms have been implemented on the now called general purpose graphics processing units (GPGPUs). While the results show that GPGPUs regularly outperform CPU based implementations, the question arose how optical flow algorithms can be ported to graphics hardware. To answer the question, the optimal algorithm structure to maximize the performance gained by using graphics cards has to be found. In this paper we compare the performance of two algorithms that are highly different in structure, implemented on both CPU and graphics hardware. Analyzing the results of the CPU and GPGPU implementation, we explore the mapping of the algorithms to the graphics hardware and thereof extract information about a preferred structure of optical flow algorithms for GPGPU based implementation

Why it matters

OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Since graphics cards have become programmable the recent years, numerous computationally intensive algorithms have been implemented on the now called general purpose graphics processing units (GPGPUs). While the results show that GPGPUs regularly outperform CPU based implementations, the question arose how optical flow algorithms can be ported to graphics hardware. To answer the question, the optimal algorithm structure to maximize the performance gained by using graphics cards has to be found. In this paper we compare the performance of two algorithms that are highly different in structure, implemented on both CPU and graphics hardware. Analyzing the results of the CPU and GPGPU implementation, we explore the mapping of the algorithms to the graphics hardware and thereof extract information about a preferred structure of optical flow algorithms for GPGPU based implementation

Key concepts: Computer science, General-purpose computing on graphics processing units, Porting, Graphics, Graphics hardware, CUDA, Real-time computer graphics, Parallel computing

Related papers

Back to paper searchBrowse research topicsOriginal source
Performance of Optical Flow Techniques on Graphics Hardware — Research Paper | ScholarLens