Enhanced Gaussian background modeling algorithm and implementation in FPGA for real-time moving object detection in surveillance video

Simple item page
dc.contributor.advisorZhang, Yun
dc.contributor.advisorKaye, Mary
dc.contributor.authorGuo, Ge
dc.date.accessioned2023-03-01T16:18:56Z
dc.date.available2023-03-01T16:18:56Z
dc.date.issued2014
dc.date.updated2023-03-01T15:01:30Z
dc.description.abstractA real-time solution of moving object detection (MOD) in surveillance video was explored in this work motivated by the practical ileed of real-time automated video analysis system. The core element of a moving object detection process is its background modeling algorithm in the content of surveillance and road monitoring applications. By reviewing and analyzing previous works, single Gaussian (SG) background modeling algorithm was selected and enhanced. Then a circuit that performs MOD with enhanced SG algorithm was designed and implemented in a Virtex6 FPGA of a ML605 evaluation board with other hardware components. The experiment results showed that the proposed MOD system could perform real-time MOD in a video of 1280x720p@30fps. It outperforms the software experiments/implementations and the state-of-art FPGA-based implementations.
dc.description.copyright© Ge Guo, 2014
dc.formattext/xml
dc.format.extentx, 108 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13465
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineGeodesy and Geomatics Engineering
dc.titleEnhanced Gaussian background modeling algorithm and implementation in FPGA for real-time moving object detection in surveillance video
dc.typemaster thesis
thesis.degree.disciplineGeodesy and Geomatics Engineering
thesis.degree.fullnameMaster of Science in Engineering
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.E.

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
item.pdf
Size:
13.87 MB
Format:
Adobe Portable Document Format
Download

Collections

Open Theses & Dissertations