1. Robust Object Segmentation Using Adaptive Thresholding Xiaxi Huang and Nikolaos V. Boulgouris International Conference on Image Processing 2007

2. Outline Introduction Proposed algorithm Experimental results Conclusions

3. Introduction(1/2) The extraction of moving objects from video sequences is important!! Object detection methods SGM ( Single Guassian Model ) MGM ( Mixed Guassian Model ) BG substaction combined color and edge information. (Aug. 2000) Serious drawbacks Poor performance for indoor shadow, light reflection, and high similarity of FG and BG.

4. Introduction(2/2) Proposed algorithm Adaptive thresholding detection Shadow removal method

5. Proposed Algorithm Procedures Of Algorithm Updating BG Image Initial Mask Estimate FG Areas Estimate BG Areas Confidence Map From Detection Of RGB Confidence Map From Detection Of Edge Maximum Of The Confidence Maps Minimum Of The Confidence Maps Combined Confidence Map Hysteresis Thresholding FG Map (Object and Shadows) Edge Map (Boundary Of objects) Post- processing Final Object Map Next Frame Background Substraction Shadow Removal

6. Proposed Algorithm Background Updating Why? In many background substraction method update all pixels in a frame. A serious drawback To avoid this condition Misclassfied a stop moving object.

7. Proposed Algorithm Initial Block-size Mask(1/5) Roughly determine the foreground areas. Lower threshold Calculate average different between the current frame and background frame in a block Threshold it with T

8. Proposed Algorithm Initial Block-size Mask(2/5) Divide the blocks with larger difference which are assumed to contain foreground pixels into smaller size Apply a higher threshold and detect sub- blocks

9. Proposed Algorithm Initial Block-size Mask(3/5) The block with larger difference Using a higher threshold …

10. Proposed Algorithm Initial Block-size Mask(4/5) How to get a foreground blocks map? Median filter Edge pixels of the objects might be lost Apply two initial block-size, and combine their foreground map

11. Proposed Algorithm Initial Block-size Mask(5/5)

12. Proposed Algorithm Color Change Detection With Adaptive Threshold(1/2) Adaptive threshold D : difference frame ( D = | I - B|) Local variance in the current frame Local variance in the difference frame Local mean value in the difference frame

13. Proposed Algorithm Color Change Detection With Adaptive Threshold(2/2) Get a threshold T by setting k 1, k 2 Create confidence maps in three color channels respectively Maximum the confidence map CMap color

14. Proposed Algorithm Edge Detection(1/2) In order to － Extraction of the foreground Removal of shadow Compute edge magnitude G x and G y are the horizontal and vertical difference in the difference frame D. Sobel mask

15. Proposed Algorithm Edge Detection(2/2)

16. Proposed Algorithm Combination(1/3) Combine two confidence maps. Estimate foreground area Maximum the confidence map Estimate background area Minimum the confidence map

17. Proposed Algorithm Combination(2/3) Combination

18. Proposed Algorithm Combination(3/3) (a)(b) (c) (d) Fig. (a) Original image, (b) Confidence map Of RGB change Detection with adaptive threshold, (c) Confidence map of Sobel edge detection, (d) Combined confidence Map.

19. Proposed Algorithm Hysteresis thresholding Remove false positive Set two thresholds T 0, T 1 ; T 1 /T 0 is about 2 or 3 C(x) > T1 : High confidence region T0 < C(x) < T1 : Check neighbors Hysteresis thresholding

20. Proposed Algorithm Shadow Removal And Post-Processing(1/4) Indoor environment Soft colored illumination, light-reflect floor, shadow Hard to distinguish shadows from objects by using color information. How to solve this problem? Combine FG and edge confidence map

21. Proposed Algorithm Shadow Removal And Post-Processing(2/4) Apply hysteresis thresholding to edge confidence map Set bounding boxes Remove pixels out of bounding boxes

22. Proposed Algorithm Shadow Removal And Post-Processing(3/4) (a) (b) (d)(c) Fig. (a) Foreground map before shadow removal, (b) Hysteresis thresholding result of edge confidence Map, (c) Foreground map after shadow removal, (d) Binary map of extracted objects.

23. Proposed Algorithm Shadow Removal And Post-Processing(4/4) Some temporal filters of offline detection To achieve above － Eliminate spurious points Retrieve missed FG pixels

24. Experimental Results Fig. (a) Origin image, (b) Foreground maps created by MGM plus HSV method, (c) Foreground maps created by mixture Gaussian Model, (d) Foreground maps created by proposed algori- thm. (a) (b) (d)(c)

25. Experimental Results Fig. (a) Origin image, (b) Foreground maps created by MGM plus HSV method, (c) Foreground maps created by mixture Gaussian Model, (d) Foreground maps created by proposed algori- thm. (a) (b) (d)(c)

26. Experimental Results Fig. (a) Origin image, (b) Foreground maps created by MGM plus HSV method, (c) Foreground maps created by mixture Gaussian Model, (d) Foreground maps created by proposed algori- thm. (a) (b) (d)(c)

27. Experimental Results Fig. (a) Origin image, (b) Foreground maps created by MGM plus HSV method, (c) Foreground maps created by mixture Gaussian Model, (d) Foreground maps created by proposed algori- thm. (a) (b) (d)(c)

28. Conclusions Compared with the popular MGM object segmentation method and the HSV shadow removal method, proposed method achieves more robust performance Considering that the proposed algorithm does not involve future frames, it can be used in real-time processing applications. Furthermore, if it is used offline, a temporal filter can be applied to further improve the performance of the algorithm.