東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Illumination compensation in video s...

Bales, Michael Ryan.

Linked to FindBook

Google Book

Amazon

博客來

Illumination compensation in video surveillance analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Illumination compensation in video surveillance analysis./
Author:	Bales, Michael Ryan.
Description:	152 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6221.
Contained By:	Dissertation Abstracts International72-10B.
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3464041
ISBN:	9781124758916

Illumination compensation in video surveillance analysis.
Bales, Michael Ryan.

Illumination compensation in video surveillance analysis. - 152 p.

Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6221.

Thesis (Ph.D.)--Georgia Institute of Technology, 2011.

Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model---called BigBackground---is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.

ISBN: 9781124758916Subjects--Topical Terms:

1669061
Engineering, Computer.

Illumination compensation in video surveillance analysis.
LDR:02834nam a2200277 4500 001 1962562
005 20140819094507.5
008 150210s2011 ||||||||||||||||| ||eng d
020 $a 9781124758916
035 $a (MiAaPQ)AAI3464041
035 $a AAI3464041
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bales, Michael Ryan. $3 2098653
245 1 0 $a Illumination compensation in video surveillance analysis.
300 $a 152 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-10, Section: B, page: 6221.
500 $a Adviser: D. Scott Wills.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2011.
520 $a Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model---called BigBackground---is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.
590 $a School code: 0078.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0464
690 $a 0544
710 2 $a Georgia Institute of Technology. $3 696730
773 0 $t Dissertation Abstracts International $g 72-10B.
790 $a 0078
791 $a Ph.D.
792 $a 2011
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3464041