1. Efficient Moving Object Segmentation Algorithm Using Background Registration Technique Shao-Yi Chien, Shyh-Yih Ma, and Liang-Gee Chen, Fellow, IEEE Hsin-Hua Lee IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, JULY 2002

2. Outline Introduction Segmentation Algorithm Shadow Effects Experimental Results Conclusion

3. Introduction Video segmentation is a key operation for content- based video coding. For example, MPEG-4 enables the content-based functionalities by using VOP (video object plane) as the basic coding element. The authors propose an efficient algorithm suitable for real-time content-based multimedia communication system.

4. Introduction (Cont.) Conventional video segmentation algorithm can be roughly classified into two categories by their primary segmentation criteria. spatial homogeneity Track the object boundary more precisely than other methods, but computation complexity is very high. change detection

5. Change detection Conventional main steps 1.Position and shape of the moving object is detect from the frame difference of two consecutive frames. 2.Boundary fine-tuning process based on spatial and temporal information. It’s thought that these approach is more efficient than the previous category because it is the motion that distinguishes a moving object from the background.

6. Segmentation Algorithm The basic idea of the proposed segmentation algorithm is change detection. The authors construct and maintain up-to-date background information from the video sequence and compare each with the background. Any pixel that is significant different from the background is assumed to be in the object region.

7. Segmentation Algorithm (Cont.)

8. Frame Difference Stationary background the characteristics is well known and more reliable. Long-term behavior the object motion accumulated from several frames instead of relying on frame difference of two consecutive frames only.

9. Frame Difference Frame difference mask. (a)(c) The original image. (b)(d) Frame difference mask

10. Background Registration Construct a reliable background information Maintain Stationary Map If the value in the stationary map exceeds a predefined value, then the pixel value in the current frame is copied to the corresponding pixel in the background buffer. The value in the background registration mask indicates that whether the background information of the corresponding pixel exists or not.

11. Background Registration (Cont.) Construction and updating of the background buffer. (a)(c) Original frame (b)(d) Constructed background

12. Background Difference Generates a background difference mask by thresholding the difference between the current frame and the background information stored in the background buffer.

13. Object Detection

14. Post Processing Remove noise regions and to smooth the object boundary. Small region filtering Close-open operation

15. Post Processing Effect of noise region elimination. (a) Mask after small-region filtering. (b) Final object mask after close-open operation.

16. Post Processing (Cont.) (c) Initial object mask. (d) Final object mask after the noise region elimination step.

17. Post Processing (Cont.) (e) Original image. (f) Segmented object.

18. Shadow Effects In situations where object shadows appear in the background region, a pre-processing gradient filter is applied on the input image to reduce the shadow effect.

19. Shadow Effects (Cont.)

20. Effect of gradient filter. (a) Original image. (b) Segmentation result of the original image. (c) Gradient image after applying the morphological gradient operation. (d) Segmentation result of the gradient image.

21. Experimental Results (Objective Evaluation ) Error rate in each frame of the Weather sequence (CIF).

22. Experimental Results (Subjective Evaluation )

23. Experimental Results (Subjective Evaluation ) (Cont.)

24. Conclusion In this paper, the author proposed an efficient moving segmentation algorithm by avoiding the use of computation intensive operations. The experimental results demonstrate that good segmentation quality can be obtained efficiently; therefore, this algorithm is very suitable for the real- time VOP generation in MPEG-4 multimedia communication systems.