1

Lucia Maddalena and Alfredo Petrosino, Senior Member, IEEE

A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

Adviser : Chih-Hung Lin Speaker : Kuan-Ju ChenDate 　　 : 2009/04/06

2

AuthorLucia Maddalena

received the Laurea degree (cum laude) in mathematics and the Ph.D. degree in applied mathematics and computer science from the University of Naples Federico II, Naples, Italy.

Alfredo Petrosino (SM’02) is an Associate Professor of

computer science at the University of Naples Parthenope, Naples, Italy.

3

OUTLINE

INTRODUCTION1

METHOD2

EXPERIMENTAL RESULTS3

CONCLUSION4

4

1.INTRODUCTION

VISUAL surveillance is a very active research area in computer vision

The main tasks in visual surveillance systems motion detection object classification Tracking activity understanding semantic description

5

1.INTRODUCTIONThe usual approach to moving object

detection is through background subtraction

Compared to other approaches, The main problem is its sensitivity to dynamic scene changes light changes moving background cast shadows

6

1.INTRODUCTIONBackground subtraction:

Unimodal versus multimodal: Recursive: Pixel-based :

7

1.INTRODUCTIONUnimodal and multimodal:

Basic background models assume that the intensity values of a pixel can be modeled

• low complexity• cannot handle moving backgrounds

8

1.INTRODUCTIONRecursive

recursively update a single background model based on each input frame.

• Space complexity is lower• Background model is carried out for a long time

period

9

1.INTRODUCTIONPixel-based :

assume that the time series of observations is independent at each pixel

10

1.INTRODUCTIONOur approach is based on the

background model automatically generated by a self-organizing method and can be broadly classified as multimodal,

recursive, and pixelbased.

11

2.METHOD

Initial Background Model1

Subtraction and Update of the Background Model2

12

2.1 Initial Background Model

a b c

d e f

a1 a2 a3

a4 a5 a6

a7 a8 a9

b1 b2 b3

b4 b5 b6

b7 b8 b9

c1 c2 c3

c4 c5 c6

c7 c8 c9

d1 d2 d3

d4 d5 d6

d7 d8 d9

e1 e2 e3

e4 e5 e6

e7 e8 e9

f1 f2 f3

f4 f5 f6

f7 f8 f9

Let be HSV components , ex: a1=(h,s,v)

13

2.2 Subtraction of the Background Model

Input pixel tp

Build pixel model C(2.1)

Find best Match Cm

Cm is found

B(Pt)=0

NO

Pt is shadow

YES

B(Pt)=1

YES

NO

LastFrameNot LastFrame

Current Frame+1

Over

LastFrame

Self-Organizing Background Subtraction

Update Background

Model

14

2.2 Subtraction of the Background Model

Use Euclidean distance to computeCn and Cother pixel distance

),(min),(

sinsincoscos),(

2,,1

222

tini

tm

jijjjiiijjjiiiji

pcdpcd

vvhsvhsvhsvhsvppd

15

2.2 Subtraction of the Background Model

16

2.2 Update of the Background Model

If best match cm Weight vector At to update in the

neighborhood cm

If best match cm isn`t found Not update

17

2.2 Update of the Background Model

a b c

d e f

a1 a2 a3

a4 a5 a6

a7 a8 a9

b1 b2 b3

b4 b5 b6

b7 b8 b9

c1 c2 c3

c4 c5 c6

c7 c8 c9

d1 d2 d3

d4 d5 d6

d7 d8 d9

e1 e2 e3

e4 e5 e6

e7 e8 e9

f1 f2 f3

f4 f5 f6

f7 f8 f9If best match cm

Computer the weight vector to update background

18

2.2 Update of the Background Model

a1 a2 a3

a4 a5 a6

a7 a8 a9

b1 b2 b3

b4 b5 b6

b7 b8 b9

c1 c2 c3

c4 c5 c6

c7 c8 c9

d1 d2 d3

d4 d5 d6

d7 d8 d9

e1 e2 e3

e4 e5 e6

e7 e8 e9

f1 f2 f3

f4 f5 f6

f7 f8 f9

),(),())(1(),( ,1, yxpjiAtjiA tjitjit

19

SHADOW DETECTION

Foreground

Ayalyze Hue-Saturation-Value(HSV) color space

shadow mask:

otherwise 0

) ( )( )( 1SP s H

Hi

Ht

Si

StV

i

Vt

t cpcpc

pif

p

define a darkening effect of shadowsidentifying as shadows those points

average image luminance

Following three condition to mask shadow

20

3.EXPERIMENTAL RESULTS

(a) original frame;(b) computed moving object detection mask(c) background model(d) background model change mask from previous frame

21

3.EXPERIMENTAL RESULTS

(a) original frame;(b) computed moving object detection mask

22

3.EXPERIMENTAL RESULTS

fpfntp

tp

F

fptp

tp

fntp

tp

Similarity

PrecisionRecall

PrecisionRecall2

Precision

Recall

1

23

3.EXPERIMENTAL RESULTS

(a) test image (b) ground truth(c) SOBS result (d) Pfinder result(e) VSAM result (f) CB result

24

4.CONCLUSIONThis paper also includes a

comprehensive accuracy testing, performed with both pixel-based and frame-based metrics Experimental results, using different sets of

data and comparing different methods, have demonstrated the effectiveness of the proposed approach

• illumination changes• cast shadows

ONGOING WORK improve detection results

25

www.themegallery.com