Latency insensitive task scheduling for real-time video processing and streaming

Xu, RYD; Jin, JS

Latency insensitive task scheduling for real-time video processing and streaming

Xu, RYD Jin, JS

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3708 LNCS pp. 387 - 394
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Xu, RYD	en_US
dc.contributor.author	Jin, JS	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2005, 3708 LNCS pp. 387 - 394	en_US
dc.identifier.isbn	354029032X	en_US
dc.identifier.isbn	9783540290322	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4033
dc.description.abstract	In recent times, computer vision and pattern recognition (CVPR) technologies made automatic feature extraction, events detection possible in real-lime, on-the-fly video processing and streaming systems. However, these multiple and computational expensive video processing tasks require specialized processors to ensure higher frame rate output. We propose a framework for achieving high video frame rate using a single processor high-end PC while multiple, computational video tasks such as background subtraction, object tracking, recognition and facial localization have been performed simultaneously. We show the framework in detail, illustrating our unique scheduler using latency insensitive tasks distribution and the execution coulent parameters generation function (PGF). The experiments have indicated successful results using high-end consumer type PC. © Springer-Verlag Berlin Heidelberg 2005.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Latency insensitive task scheduling for real-time video processing and streaming	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3708 LNCS	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
dc.location.activity	Belgium	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	3708 LNCS	en_US

Abstract:

In recent times, computer vision and pattern recognition (CVPR) technologies made automatic feature extraction, events detection possible in real-lime, on-the-fly video processing and streaming systems. However, these multiple and computational expensive video processing tasks require specialized processors to ensure higher frame rate output. We propose a framework for achieving high video frame rate using a single processor high-end PC while multiple, computational video tasks such as background subtraction, object tracking, recognition and facial localization have been performed simultaneously. We show the framework in detail, illustrating our unique scheduler using latency insensitive tasks distribution and the execution coulent parameters generation function (PGF). The experiments have indicated successful results using high-end consumer type PC. © Springer-Verlag Berlin Heidelberg 2005.

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http://hdl.handle.net/10453/4033