Detection of salient events in large datasets of underwater video

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dc.contributor.authorGebali, Aleya
dc.contributor.supervisorBranzan Albu, Alexandra
dc.date.accessioned2012-08-23T18:54:23Z
dc.date.available2012-08-23T18:54:23Z
dc.date.copyright2012en_US
dc.date.issued2012-08-23
dc.degree.departmentDept. of Electrical and Computer Engineeringen_US
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractNEPTUNE Canada possesses a large collection of video data for monitoring marine life. Such data is important for marine biologists who can observe species in their natural habitat on a 24/7 basis. It is counterproductive for researchers to manually search for the events of interest (EOI) in a large database. Our study aims to perform the automatic detection of the EOI de ned as animal motion. The output of this approach is in a summary video clip of the original video fi le that contains all salient events with their associated start and end frames. Our work is based on Laptev [1] spatio-temporal interest points detection method. Interest points in the 3D spatio-temporal domain (x,y,t) require frame values in local spatio-temporal volumes to have large variations along all three dimensions. These local intensity variations are captured in the magnitude of the spatio-temporal derivatives. We report experimental results on video summarization using a database of videos from Neptune Canada. The eff ect of several parameters on the performance of the proposed approach is studied in detail.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/4156
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectComputer Visionen_US
dc.subjectVideo Abstracten_US
dc.titleDetection of salient events in large datasets of underwater videoen_US
dc.typeThesisen_US

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