1. MOVING OBJECTS SEGMENTATION AND ITS APPLICATIONS

2. Proposed Algorithm 1.smoothing process 2.moving algorithm 3.template matching scheme 4.background estimation 5.post-processing

3. Smoothing Processing 取出 Y, C b, C r

4. Smoothing Processing Median filtering to smooth Y Result the processed Y’

5. Smoothing Processing

6. Moving Object Segmentation Adopt a spatial-temporal approach to segment object X-y-t to x-t image

7. 3D-2D Y = 179 Row data of x-t means a pixel 320*240*180 180*320

8. Moving or static pixel

9. Refinement algorithm M1(x,t), M2(x,t) and M3(x,t) correspond to red, green and blue channels moving (f(x,t)=1) or static (f(x,t)=0)

10. Refinement algorithm L pixels (L frame length) in a row data

11. Minimun squared error The problem of Eq.(5) is solved by using the pseudoinverse operation, which is based on minimum squared-error (MSE) method [8]. The solution W is formulated as,

12. Pseudoinverse M † is called the pseudoinverse of matrix M defined as,

13. Moving or static pixel 原:原: 改:改: Moving piexl static piexl

14. Threshold calculate the means μ and variances σ 2 2 of state values pixel State value

15. Gaussian distribution of two states Probability,p(x|s) State value Static pixel Moving pixel

16. Discriminate function g(x) Threshold = 0.39m Weighting value ： [ ω 1, ω 2, ω 3 ] =[0.0002,-0.0326,0.0315]

17. X-T marked graph

18. X-Y marked graph Original x-y marked image

19. Multiple object detection Start frame End frame

20. Search template Color different

21. Search template u,v 搜尋範圍

22. Search template-min Then refine the marked values b(x,y) of current frame,

23. Background estimation Based on x-t sliced image If moving pixel a(x,t)=1 If static pixel a(x,t)=0

24. Post-processing => By template

25. Morphology modification

26. Result

28. Video edit

30. END