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Real-time fire detection in low qual...

True, Nicholas James.

Real-time fire detection in low quality video.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Real-time fire detection in low quality video./
Author:	True, Nicholas James.
Description:	1 online resource (78 pages)
Notes:	Source: Masters Abstracts International, Volume: 48-06, page: 3728.
Contained By:	Masters Abstracts International48-06.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781124068121

Real-time fire detection in low quality video.
True, Nicholas James.

Real-time fire detection in low quality video. - 1 online resource (78 pages)

Source: Masters Abstracts International, Volume: 48-06, page: 3728.

Thesis (M.S.)--University of California, San Diego, 2010.

Includes bibliographical references

For over fifty years, simple smoke and heat sensors have been the primary means of automated fire detection. We are now at the point where computer processing power is cheap enough and machine vision technology is sophisticated enough for a new generation of automated fire detection systems: video-based fire detection (VBFD). While current smoke and fire detection technology has proven to be reliable and effective, VBFD technology promises to go where existing systems can't and to detect fires faster than its venerable predecessors ever could.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781124068121Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Real-time fire detection in low quality video.
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100 1 $a True, Nicholas James. $3 1189478
245 1 0 $a Real-time fire detection in low quality video.
264 0 $c 2010
300 $a 1 online resource (78 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 48-06, page: 3728.
500 $a Adviser: Serge Belongie.
502 $a Thesis (M.S.)--University of California, San Diego, 2010.
504 $a Includes bibliographical references
520 $a For over fifty years, simple smoke and heat sensors have been the primary means of automated fire detection. We are now at the point where computer processing power is cheap enough and machine vision technology is sophisticated enough for a new generation of automated fire detection systems: video-based fire detection (VBFD). While current smoke and fire detection technology has proven to be reliable and effective, VBFD technology promises to go where existing systems can't and to detect fires faster than its venerable predecessors ever could.
520 $a This thesis explores a few methods for achieving real-time video-based fire detection in low quality data. Assuming a stationary source camera, we describe an algorithm that uses a support vector machine to classify short, targeted video sequences as fire/non-fire. The algorithm achieves a classification rate of 96.0% on a holdout set of real world data. Furthermore, the system is robust with respect to the distance from the fire source, works day or night, and only requires the processing power of a common desktop computer.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Computer Science and Engineering. $3 1189479
773 0 $t Masters Abstracts International $g 48-06.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1477940 $z click for full text (PQDT)

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