1. Video Segmentation Based on Image Change Detection for Surveillance Systems Tung-Chien Chen (djchen@soe.ucsc.edu) EE 264: Image Processing and Reconstruction

2. Outline Background –Image Change Detection –Video Surveillance Systems Implementation –Block diagram and algorithm description Demo Comment

3. Image Change Detection Differencing Significance and hypothesis tests Predictive models Shading Models Background Models Change mask consistency and post processing ….. Video surveillance Remote sensing Medical diagnosis and treatment, Civil infrastructure, Underwater sensing, Driver assistance systems ……

4. In My Project Differencing Significance and hypothesis tests Predictive models Shading Models Background Models Change mask consistency and post processing ….. Video surveillance Remote sensing Medical diagnosis and treatment, Civil infrastructure, Underwater sensing, Driver assistance systems ……

5. Video Surveillance Systems A technological tool that assists humans by providing an extended perception and reasoning capability about situations of interest that occur in the monitored environments

6. Video Surveillance Systems A technological tool that assists humans by providing an extended perception and reasoning capability about situations of interest that occur in the monitored environments

7. Reference Paper Efficient moving object Segmentation Algorithm Using Background Registration Technique S-Y Chien, S-Y Ma, and L-G Chen, IEEE Fellow @ National Taiwan University IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2002

8. Block Diagram of the Framework

9. Step1 – Differencing (1/2) Frame difference and thresholding –Difference between current frame and previous frame FD: frame difference FDM: frame difference mask

10. Step1 – Differencing (2/2) Background differencing and thresholding –Difference between current frame and background BD: background difference BDM: background difference mask

11. Step2 – Background Registration According to FDM, pixels not moving for a long time are considered as reliable background pixels SI: Stationary index BI: Background indicator BG: Background information

12. Example of Background Registration (1/2)

13. Example of Background Registration (2/2) Include the function of background updating

14. Step2- Object Detection and Initial Object Mask Generation Object detection –Produce “Initial object mask” (IOM)

15. Object Detection Look up table for object detection

16. Step4- Post-processing Two main parts in post-processing: –Noise region elimination and boundary smoothing Connected component algorithm to eliminate small regions Morphological close–open operations are applied to smooth the object boundary

17. Example of Post Operation Initial Object MaskAfter Connect Component After Close-open OperationFinal Object

18. Results and Demo

20. Result Demo

21. Comments (1/2) For change detection based segmentation algorithm for surveillance system –Speed is high, but not robust –Performance degrade with the uncovered background situation, still object situation, light changing, shadow, and noise –Post-process can promote, but lose efficiency –Should automatically decide the thresholds –Some limitations: strong change in light source, difference luminance between background foreground, camera moving/zoom/rotation, foreground object should move

22. Reference [1] R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam “Image Change Detection Algorithms: A Systematic Survey,” IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294–303, March. 2005. [2] R. Collins, A. Lipton, and T. Kanade, “Introduction to the special section on video surveillance,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 745–746, Aug. 2000. [3] C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 747–757, Aug. 2000. [4] C. R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 780–785, Jul. 1997. [5] R. Mech and M. Wollborn, “A noise robust method for 2D shape estimation of moving objects in video sequences considering a moving camera,” Signal Process., vol. 66, 1998. [6] S.-Y.Ma, S.-Y. Chien, and L.-G. Chen, “An efficient moving object segmentation algorithm for MPEG-4 encoding systems,” in Proc. Int. Symp. Intelligent Signal Processing and Communication Systems 2000, 2000. [7] S. Y. Chien, S. Y. Ma, and L. G. Chen “Efficient Moving Object Segmentation Algorithm Using Background Registration Technique,” IEEE Trans. on circuits and system for video technology, vol. 12, no. 7, pp. 577–586, JULY. 2002. [8] R. M. Haralick and L. G. Shapiro, Computer and Robot Vision. Reading, MA: Addison- Wesley, 1992. [9] J. Serra, Image Analysis and Mathematical Morphology. London, U.K.: Academic, 1982.

23. Thanks for listening !! Questions ?