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Egocentric View Transition for Video Monitoring in a Distributed Camera Network

Egocentric View Transition for Video Monitoring in a Distributed Camera Network. Chairman:Hung -Chi Yang Presenter: Fong- Ren Sie Advisor: Yen-Ting Chen Date: 2013.3.20. Kuan-Wen Chen, Pei- Jyun Lee, and Yi-Ping Hung, Department of Computer Science and Information Engineering

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Egocentric View Transition for Video Monitoring in a Distributed Camera Network

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  1. Egocentric View Transition forVideo Monitoring in a Distributed Camera Network Chairman:Hung-Chi Yang Presenter: Fong-RenSie Advisor: Yen-Ting Chen Date: 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

  2. Outline • Introduction • Methodology • Results • Conclusions • References

  3. Introduction • Multi-camera systems used in video surveillance applications • Airport • Railway security • Traffic monitoring

  4. Introduction • Multi-camera system • Advantage • Can monitor the activities of targets over a large area • Show multiple video streams on display simultaneously

  5. Introduction • Multi-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.

  6. Introduction • Egocentric 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

  7. Methodology • The basic concept of view transition comes from view morphing • Virtual teleconference system • Sports broadcasting system • Photo browsing and exploring system

  8. Methodology • To 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

  9. Methodology • Multi-camera Tracking • In the overlapping case • Multi-cameratracking 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 bothspatio-temporal and appearance cues.

  10. 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

  11. Conclusions • Egocentric view transition which synthesizes thevirtual views when switching cameras • Overlapping FOVs of cameras. • presented a framework to build a foreground billboard and put it to the 3D model.

  12. Conclusions • Non-overlappingFOVs 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

  13. 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) • 2. Debevec, P.E., Taylor, C.J., Malik, J.: Modeling and Rendering Architecture from Photographs: A Hybrid Geometry- and Image-Based Approach. In: SIGGRAPH (1996) • 3. Finke, R.A.: Principles of Mental Imagery. MIT Press, Cambridge (1989) • 4. Girgensohn, A., Kimber, D., Vaughan, J., Yang, T., Shipman, F., Turner, T., Rieffel, E., Wilcox, L., Chen, F., Dunnigan, T.: DOTS: Support for Effective Video Surveillance. In: • MULTIMEDIA (2007) • 5. Hsiao, C.H., Huang, W.C., Chen, K.W., Chang, L.W., Hung, Y.P.: Generating Pictorial-Based Representation of Mental Image for Video Monitoring. In: IUI (2009) • 6. Horprasert, T., Harwood, D., Davi, L.: A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection. In: FRAME-RATE Workshop (1999)

  14. References • 7. Haan, G., Scheuer, J., Vries, R., Post, F.H.: Egocentric Navigation for Video Surveillance in 3D Virtual Environments. In: IEEE Symposium on 3D User Interfaces (2009) • 8. Hartley, R.I., Zisserman, A.: Multiple View Geometry, 2nd edn. Cambridge University Press, Cambridge (2004) • 9. Katkere, A., Moezzi, S., Kuramura, D.Y., Kelly, P., Jain, R.: Towards Video-Based Immersive Environments. Multimedia System 5(2), 69–85 (1997) • 10. Kanade, T., Narayanan, P., and Rander, P.: Virtualized Reality: Concept and Early Results. Tech. Rep. CMU-CS-95-153 (1995) • 11. Levenberg, K.: A method for the solution of certain problems in least squares. Quarterly Applied Math. 2, 164–168 (1944) • 12. Lei, B., Hendriks, E.: Real-Time Multi-Step View Reconstruction for a Virtual Teleconference System. EURASIP J. Appl. Signal Process 2002(10), 1067–1088 (2002)

  15. References • 13. Neumann, U., You, S., Hu, J., Jiang, B., Lee, J.: Augmented Virtual Environment (AVE): Dynamic Fusion of Imagery and 3d Models. In: IEEE Virtual Reality (2003) • 14. Ohta, Y., Kitahara, I., Kameda, Y., Ishikawa, H., Koyama, T.: Live 3D Video in Soccer Stadium. IJCV 75(1), 173–187 (2007) • 15. Palmer, S.: Vision Science: Photons to Phenomenology. MIT Press, Cambridge (1999) • 16. Reeves, W.T.: Particle Systems - a Technique for Modeling a Class of Fuzzy Objects. ACM Transactions on Graphics 2, 91–108 (1983) • 17. Sawhney, H.S., Arpa, A., Kumar, R., Samarasekera, S., Aggarwal, M., Hsu, S., Nister, D., Hanna, K.: Video Flashlights: Read Time Rendering of Multiple Videos for Immersive Model Visualization. In: EGRW (2002) • 18. Seitz, S., Dyer, C.: View Morphing. In: SIGGRAPH (1996) • 19. Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity using Real-Time Tracking. IEEE Transactions on PAMI 22(8), 747–757 (2000)

  16. References • 20. Segal, M., Korobkin, C., Widenfelt, R., Foran, J., Haeberli, P.: Fast Shadows and Lighting Effects Using Texture Mapping. In: SIGGRAPH (1992) • 21. Snavely, N., Seitz, S.M., Szeliski, R.: Photo Tourism: Exploring Photo Collections in 3d. In: SIGGRAPH (2006) • 22. Thorndyke, P., Hayes-Roth, B.: Differences in Spatial Knowledge Acquired from Maps and Navigation. Cognitive Psychology 14(4), 560–589 (1982) • 23. Welch, G., Bishop, G.: An introduction to the kalman filter. Chapel Hill, NC, USA, Tech. Rep. (1995) • 24. Wang, Y., Krum, D.M., Coelho, E.M., Bowman, D.A.: Contextualized Videos: Combining • Videos with Environment Models to Support Situational Understanding. IEEE TVCG 13(6), 1568–1575 (2007) • 25. Zhang, Z.: A Flexible New Technique for Camera Calibration. IEEE Transactions on PAMI 22, 1330–1334 (2000)

  17. Thank you for your attention

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