1. Image Subtraction for Real Time Moving Object Extraction Shahbe Mat Desa, Qussay A. Salih, CGIV’04

2. Outline Introduction Automatic Motion Detection Background Reconstruction Noise Reduction Experiment Results Conclusion

3. Introduction In recent years, development of motion analysis in many vision systems arise researches in conjunction with the immense attentions of employing real time application to control complex real world system such as ATM security, airport surveillance, traffic monitoring, etc. The basic idea of mostly automated surveillance applications is that motion detection continuously operating and the system is triggered to perform higher- level processes such as object recognition and tracking. Standards such as MPEG-1, MPEG-2 use block-based motion compensation and DCT techniques may be time consuming and cause complex computation.

4. Objectives Automatic motion detection Reference background update Segmentation of dynamic region from static region Noise reduction Reliable and less complex Real time and auto-completion

5. Motion mask extraction Motion detection issue –As a binary labeling problem: –Automatically detecting objects

6. Motion mask extraction(2) Image subtraction –Background subtraction, d B : |f k – B| –Temporal differencing, d k-1, d k+1 : |f k-1 – f k |, |f k+1 – f k | Calculate motion mask, motion k, with threshold T d

7. Background reconstruction Background varies from time to time as the scene changes. w, h: width and height of frame dynamic_pixel: Σ (motion k ) B: background image, f 0 M: motion threshold, 4%

8. Noise reduction Two operations –Erosion followed by dilation Erosion –With structuring element E Result in a value of 1 in motion mask motion k at P=(x,y) if the spatial arrangement of ones in E P fully matches that of motion k. => Removes isolated foreground pixels

9. Noise reduction (2) Dilation –With structuring element D The result is the set of all points P=(x,y) so that reflection and motion k overlap by at least one nonzero elements. => Adds pixels to the boundary of the object

10. Result (1) Manual generate ground truth G Performance evaluator: Root Mean Square Error (RMSE) measurement G(x,y): ground truth F(x,y): output

11. Results (2) Four different-scene test sequences: –Moving vehicles on road –People walking indoors and outdoors

13. Result (3) Compared with the common background subtraction: Scene Common Method Proposed Method A0.03240.0275 B0.05240.0216 C0.12880.0611 D0.14950.0696

14. Conclusion Proposed a less-complex method that enables high-level real time processes. Compared with common background extraction method, the method is more reliable.