GPU Acceleration of Real-time Feature Based Algorithms

Feature tracking is one of the most fundamental tasks in computer vision, being used as a preliminary step to many high-level algorithms. In general, however, the number of features tracked (leading to more accurate high-level algorithms) must be balanced against the computational requirements of th...

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Bibliographic Details
Published inWMVC '07 : IEEE Workshop on Motion and Video Computing, 2007 : 23-24 February 2007 p. 8
Main Authors Ready, Jason M., Taylor, Clark N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2007
Subjects
Acceleration
Application software
Computer graphics
Computer vision
Costs
Image motion analysis
Microcomputers
Motion estimation
Parallel processing
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Online AccessGet full text
ISBN0769527930
9780769527932
DOI10.1109/WMVC.2007.17

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Summary:Feature tracking is one of the most fundamental tasks in computer vision, being used as a preliminary step to many high-level algorithms. In general, however, the number of features tracked (leading to more accurate high-level algorithms) must be balanced against the computational requirements of the feature tracking algorithm. To enable a large number of features to be tracked in real time without degrading the computational performance of high-level computer vision algorithms, we offload the feature tracking algorithm to the the video card (GPU) found in modern personal computers. Using the GPU allows for tracking an order of magnitude more features than a pure software-based algorithm, with minimal increase in CPU usage. We have demonstrated the computational benefits of GPU-based feature tracking within a real-time video stabilization application.
ISBN:0769527930
9780769527932
DOI:10.1109/WMVC.2007.17