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A Robust Background Subtraction and Shadow Detection. Proc. ACCV'2000 , Taipei, Taiwan, January 2000. 井民全. Outline. Introduction Background Modeling Pixel Classification or Subtraction Operation Automatic threshold Selection Experimental result. Introduction.

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A robust background subtraction and shadow detection

A Robust Background Subtraction and Shadow Detection

Proc. ACCV'2000 , Taipei, Taiwan, January 2000.

井民全


A robust background subtraction and shadow detection

Outline

  • Introduction

  • Background Modeling

  • Pixel Classification or Subtraction Operation

  • Automatic threshold Selection

  • Experimental result


A robust background subtraction and shadow detection

Introduction

Extracting moving objects from a video sequences

  • Application

  • What’s problem with before?

  • Requirements

  • The purpose


A robust background subtraction and shadow detection

Background

images

Current Image

Background

Moving object

Training


A robust background subtraction and shadow detection

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)


A robust background subtraction and shadow detection

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



A robust background subtraction and shadow detection

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.


A robust background subtraction and shadow detection

Different pixels yield different

distributions of illumination and

chromaticity distortion.

Using single threshold, we must do normalization


A robust background subtraction and shadow detection

Ei (expected color)

G

R

B

What’s problem of

the dark pixel ?


A robust background subtraction and shadow detection

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


A robust background subtraction and shadow detection

Automatic threshold Selection

Freq.

+

0

Normalized CD

Fig. The normalized chromaticity

distortion histogram

  • The thresholds are selected according

  • to the desired detection rate r


A robust background subtraction and shadow detection

Experimental result

Images size= 360 x 240

Detection rate= 0.9999

Lower bound of the normalized brightness distortion = 0.4


A robust background subtraction and shadow detection

Fig. A sequence of an outdoor scene

contain a person walking across

the street


A robust background subtraction and shadow detection

Fig. An application of the

background subtraction

in a motion capture system

Fig. game


A robust background subtraction and shadow detection

Fig. An application of background

subtraction in video editing


A robust background subtraction and shadow detection

Conclusion

  • Presented a background subtraction algorithm

  • Accurate, robust, reliable and efficiently computed

  • Real-time applications