1. The image based surveillance system for personnel and vehicle tracking Chairman:Hung-Chi Yang Advisor: Yen-Ting Chen Presenter: Fong-Ren Sie Date: 2014.5.21

2. Outline Introduction Paper review Results Future Work References

3. Introduction The traditional video surveillance system ◦ Closed-circuit televisions (CCTV) ◦ Digital video recorders (DVR) Disadvantages ◦ Need someone to monitor and search Real time intelligent video surveillance systems ◦ High-cost and low-efficiency

4. Introduction 4 The intelligent video surveillance system is a convergence technology ◦ Detecting and tracking objects ◦ Analyzing their movements ◦ Responding

5. Paper review(1/2) Egocentric View Transition for Video Monitoring in a Distributed Camera Network 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

6. 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

7. 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

8. 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.

9. Paper review(2/2) Extraction of moving regions by gray-scale BM

10. Paper review(2/2) The results of RGB BM according to the sensitivity parameter

11. Paper review(2/2) The 152th frame

12. 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

13. Thank you for your attention