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Egocentric View Transition forVideo Monitoring in a Distributed Camera Network
Chairman:Hung-Chi YangPresenter: Fong-Ren SieAdvisor: Yen-Ting ChenDate: 2013.3.20
Kuan-Wen Chen, Pei-Jyun Lee, and Yi-Ping Hung, Department of Computer Science and Information
Engineering Graduate Institute of Networking and Multimedia,
National Taiwan University, Taipei, Taiwan,2011
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OutlineIntroductionMethodologyResultsConclusionsReferences
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IntroductionMulti-camera systems used in
video surveillance applications◦Airport◦Railway security◦Traffic monitoring
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IntroductionMulti-camera system
◦Advantage Can monitor the activities of targets over
a large area
Show multiple video streams on display simultaneously
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IntroductionMulti-camera system
◦Disadvantage To security guards or users using the
system, with the number of video streams increasing the difficulty of monitoring increases.
The user needs to understand where the target is in the environment and the geometrical relationship between cameras.
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IntroductionEgocentric view transition
◦Avoid the effect of uncomfortable flash caused by sudden view change
◦Help users understand the spatial relationships among the target, cameras, and environments easily
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MethodologyThe basic concept of view
transition comes from view morphing ◦Virtual teleconference system◦Sports broadcasting system◦Photo browsing and exploring
system
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MethodologyTo monitor multiple cameras,
some works embedded video surveillance images in a 3D model by using projective texture mapping to integrate live video streams with the model
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MethodologyMulti-camera Tracking
◦In the overlapping case Multi-camera tracking is performed by
comparing the 3D positions estimated from each camera.
◦In the non-overlapping case which tracks targets across non-
overlapping cameras based on both spatio-temporal and appearance cues.
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Methodology
Background Texture Adaptation◦We calculate the pixel density of
texture ratio of real camera to virtual camera by the following equation:
Rr>1 → Paste with that captured by cameras
Rr<1 →use the grid-texture
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ConclusionsEgocentric view transition
which synthesizes the virtual views when switching cameras◦ Overlapping FOVs of cameras.
presented a framework to build a foreground billboard and put it to the 3D model.
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Conclusions◦Non-overlapping FOVs of cameras. a better view transition effect use a particle system to visualize the
probability distribution of where the target is in the blind region
rule of setting virtual camera positions and a background texture adaptation method
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References 1. Chen, K.W., Lai, C.C., Hung, Y.P., Chen, C.S.: An Adaptive
Learning Method for Target Tracking across Multiple Cameras. In: CVPR (2008)
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Thank you for your attention
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