Image Processing for Multiple-Target Tracking on a Graphics Processing Unit - Tapa blanda

Tanner, Michael A.

 
9781249401001: Image Processing for Multiple-Target Tracking on a Graphics Processing Unit
Tapa blanda
ISBN 10: 1249401003 ISBN 13: 9781249401001
Editorial: BiblioScholar, 2012

Sinopsis


Multiple-target tracking (MTT) systems have been implemented on many di erent platforms, however these solutions are often expensive and have long development times. Such MTT implementations require custom hardware yet o er very little flexibility with ever changing data sets and target tracking requirements. This research explores how to supplement and enhance MTT performance with an existing graphics processing unit (GPU) on a general computing platform. Typical computers are already equipped with powerful GPUs to support various games and multimedia applications. However, such GPUs are not currently being used in desktop MTT applications. Bottleneck MTT image processing functions (frame di erencing) were converted to execute on the GPU. On average, the GPU code executed 287% faster than the MATLAB implementation. Some individual functions actually executed 20 times faster than the baseline. These results indicate that the GPU is a viable source to signi cantly increase the performance of MTT with a low-cost hardware solution.

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Reseña del editor


Multiple-target tracking (MTT) systems have been implemented on many di erent platforms, however these solutions are often expensive and have long development times. Such MTT implementations require custom hardware yet o er very little flexibility with ever changing data sets and target tracking requirements. This research explores how to supplement and enhance MTT performance with an existing graphics processing unit (GPU) on a general computing platform. Typical computers are already equipped with powerful GPUs to support various games and multimedia applications. However, such GPUs are not currently being used in desktop MTT applications. Bottleneck MTT image processing functions (frame di erencing) were converted to execute on the GPU. On average, the GPU code executed 287% faster than the MATLAB implementation. Some individual functions actually executed 20 times faster than the baseline. These results indicate that the GPU is a viable source to signi cantly increase the performance of MTT with a low-cost hardware solution.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
BiblioScholar
Año de publicación
2012
Idioma
Inglés
ISBN 10 
1249401003
ISBN 13 
9781249401001
Encuadernación
Tapa blanda
Número de páginas
82
Contacto del fabricante
no disponible
Persona responsable
no disponible