A Robust Background Subtraction and Shadow Detection

1. A Robust Background Subtraction and Shadow Detection Proc. ACCV'2000 , Taipei, Taiwan, January 2000. 井民全

2. Outline • Introduction • Background Modeling • Pixel Classification or Subtraction Operation • Automatic threshold Selection • Experimental result

3. Introduction Extracting moving objects from a video sequences • Application • What’s problem with before? • Requirements • The purpose

4. Background images Current Image Background Moving object Training

5. The purpose 1. Static background 2. Using color information 3. New color model α>1 <1 =1 Color model Brightness distortion G Ei (expected color) αi Ei Cdi=color distortion R B Ii ( current color)

6. Background Modeling(training) • A pixel is modeled by a 4-tuple <Ei,si, i ,bi> • Ei = arithmetic means rgb value over n frame • si = standard deviation of rgb value over n frame • i = variation of the brightness distortion • bi = variation of the chromaticity distortion N=background frames

7. -normalized color bands in the brightness distortion and chromaticity distortion.

8. Pixel Classification or Subtraction Operation • Original background (B): brightness and chromaticity • similar to the trained background. • Shaded background or shadow(S): similar chromaticity • but lower brightness. • Highlighted background(H): similar chromaticity but • lower brightness. • Moving foreground object(F): chromaticity different from • from the expected values in trained background.

9. Different pixels yield different distributions of illumination and chromaticity distortion. Using single threshold, we must do normalization

10. Ei (expected color) G R B What’s problem of the dark pixel ?

11. Automatic threshold Selection Total sample=NXY N=background frames Freq. - + 0 Normalized Ahpha value Fig. The normalized brightness distortion histogram • The thresholds are selected according • to the desired detection rate r

12. Automatic threshold Selection Freq. + 0 Normalized CD Fig. The normalized chromaticity distortion histogram • The thresholds are selected according • to the desired detection rate r

13. Experimental result Images size= 360 x 240 Detection rate= 0.9999 Lower bound of the normalized brightness distortion = 0.4

15. Fig. A sequence of an outdoor scene contain a person walking across the street

17. Fig. An application of the background subtraction in a motion capture system Fig. game

18. Fig. An application of background subtraction in video editing

19. Conclusion • Presented a background subtraction algorithm • Accurate, robust, reliable and efficiently computed • Real-time applications