Visualvital: An Observation Model for Multiple Sections of Scenes

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dc.contributor.VIAF50151836493420401232 (Hamlen, KW)
dc.contributor.authorDuan, Jun
dc.contributor.authorZhang, Kang
dc.contributor.authorHamlen, Kevin W.
dc.contributor.utdAuthorDuan, Jun
dc.contributor.utdAuthorZhang, Kang
dc.contributor.utdAuthorHamlen, Kevin
dc.date.accessioned2019-10-25T21:47:11Z
dc.date.available2019-10-25T21:47:11Z
dc.date.created2018-08-04
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided Link to Article).
dc.description.abstractA computational methodology for reorienting, repositioning, and merging camera positions within a region under surveillance is proposed, so as to optimally cover all features of interest without overburdening human or machine analysts with an overabundance of video information. This streamlines many video monitoring applications, such as vehicular traffic and security camera monitoring, which are often hampered by the difficulty of manually identifying the few specific locations (for tracks) or frames (for videos) relevant to a particular inquiry from a vast sea of hundreds or thousands of hours of video. VisualVital ameliorates this problem by considering geographic information to select ideal locations and orientations of camera positions to fully cover the span without losing key visual details. Given a target quantity of cameras, it merges relatively unimportant camera positions to reduce the quantity of video information that must be collected, maintained, and presented. Experiments apply the technique to paths chosen from maps of different cities around the world with various target camera quantities. The approach finds detail-optimizing positions with a time complexity of O(n log n). ©2017 IEEE.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipNSF award #1054629 and AFOSR award FA9550-14-1-0173
dc.identifier.bibliographicCitationDuan, J., K. Zhang, and K. W. Hamlen. 2018. "VisualVital: An observation model for multiple sections of scenes." 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation: 1-8, doi: 10.1109/UIC-ATC.2017.8397546
dc.identifier.isbn9781538604342
dc.identifier.urihttps://hdl.handle.net/10735.1/7036
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.isPartOf2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
dc.relation.urihttp://dx.doi.org/10.1109/UIC-ATC.2017.8397546
dc.rights©2017 IEEE
dc.subjectBig data
dc.subjectCameras
dc.subjectComputer networks--Security measures
dc.subjectSmart cities
dc.subjectUbiquitous computing
dc.subjectGeographic information systems
dc.subjectVideo surveillance
dc.titleVisualvital: An Observation Model for Multiple Sections of Scenes
dc.type.genrearticle

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