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The image based surveillance system for personnel and vehicle tracking
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Transcript of The image based surveillance system for personnel and vehicle tracking
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The image based surveillance system for personnel and vehicle tracking
Chairman:Hung-Chi YangAdvisor: Yen-Ting Chen
Presenter: Fong-Ren SieDate: 2014.5.21
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Outline
IntroductionPaper reviewResultsFuture WorkReferences
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IntroductionThe traditional video surveillance
system◦Closed-circuit televisions (CCTV)◦Digital video recorders (DVR)
Disadvantages◦Need someone to monitor and
searchReal time intelligent video
surveillance systems◦High-cost and low-efficiency
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IntroductionThe intelligent video surveillance
system is a convergence technology◦Detecting and tracking objects◦Analyzing their movements◦Responding
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Paper review(1/2)Egocentric View Transition for
Video Monitoring in a Distributed Camera Network
(a) The original image(b) the image of virtual camera without grid-based visualization(c) the image of virtual
camera with grid-based visualization
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Paper review(1/2)
(a) transition from camera 1 to camera 2, (b) transition from camera 2 to camera 3, and (c) transition from camera 3 to camera 4
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Paper review(1/2)
(a) transition from camera 5 to camera 6(b) transition from camera 3 to a blind region and then back to camera 3 (c)transition from camera 3 to camera 8
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Paper review(2/2)Fast and Robust Algorithm of
Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems ◦Gray-scale BM
Image information is excessively attenuated.
◦RGB color model Very sensitive to even small changes
caused by light scattering or reflection.
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Paper review(2/2)
Extraction of moving regions by gray-scale BM
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Paper review(2/2)
The results of RGB BM according to the sensitivity parameter
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Paper review(2/2)
The 152th frame
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References [1] T. Bouwmans, “Recent Advanced Statistical Background
Modeling for Foreground Detection: A Systematic Survey,” Recent Patents on Computer Science, vol. 4, no. 3, pp. 147-176, 2011.
[2] T. Bouwmans, F. E. Baf, and B. Vachon, “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey,” Recent Patents on Computer Science, vol. 1, no. 3, pp. 219-237, 2008.
[3] T. Bouwmans, F. E. Baf, and B. Vachon, “Statistical Background Modeling for Foreground Detection: A Survey,” Handbook of Pattern Recognition and Computer Vision, vol. 4, no. 2, pp. 181-199, 2010.
[4]Jong Sun Kim, Dong Hae Yeom, and Young Hoon Joo, “Fast and Robust Algorithm of Tracking Multiple Moving Objects for Intelligent Video Surveillance Systems ”IEEE Transactions on Consumer Electronics, Vol. 57, No. 3, August, 2011
[5]Mukesh Kumar ,”A Real-Time Vehicle License Plate Recognition (LPR) System”,Thesis report ,THAPAR UNIVERSITY, PATIALA,INDIA,2009
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Thank you for your attention