poster: eii winter school 2007

Real-time background estimation for video surveillance applications Mahfuzul Haque, Manzur Murshed and Manoranjan Paul Gippsland School of Information Technology, Monash University, Victoria 3842, Australia Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au

Abstract

Background subtraction is the initial step for extracting foreground information from a video sequence in surveillance systems for

object tracking and activity recognition. Basic Background Subtraction (BBS) technique is sufficient in controlled environments

where system initialization is possible with a clear background image and where background is uncluttered and illumination change

is rare. But for real world environments adaptive background model like Gaussian Mixture Model (GMM) is used to adapt with those

situations. This research attempts to estimate the background from a given video sequence at a particular instance of time using a

modified version of the existing Gaussian Mixture Model. Instead of pixel classification as foreground or background like other

methods, the estimated background is used in background subtraction from the current frame for foreground extraction. Several

experiments with real and synthesized videos show that the proposed method performs better in background estimation and

foreground extraction where clear background is rare or never present.

Background model performance evaluation Toolkit

Sample experimental results

Conclusion

Technique

Frame 1 Frame 71 Frame 297

Input

frame

Estimated

background

Foreground

[1] Grimson, W.E.L. and C. Stauffer. Adaptive background mixture models for real-time tracking. in IEEE Conference on Computer

Vision and Pattern Recognition. 1999.

[2] KaewTraKulPong, P. and R. Bowden, An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow

Detection, in 2nd European Workshop on Advanced Video Based Surveillance Systems. 2001, Kluwer Academic Publishers.

[3] Lee, D.-S., Effective Gaussian Mixture Learning for Video Background Subtraction. IEEE Transactions on Pattern Analysis and

Machine Intelligence, 2005. 27(5): p. 827 - 832.

[4] Zhang, J. and C.H. Chen. Moving Objects Detection and Segmentation In Dynamic Video Backgrounds. in IEEE Conference on

Technologies for Homeland Security. 2007.

References

1. Background estimation model is constructed using the basic formulation

introduced by [1][2].

2. The history of each pixel {X1,X2,…,Xt} is modelled by a mixture of K Gaussian

distributions where Xt is the pixel value of {x,y} at time t.

3. The distribution parameters, ∑i,t, µi,t and wi,t are learned and updated after each

pixel observation.

4. Unlike performing pixel classification as background and foreground like existing

methods [1][2][3][4], the proposed method first estimates the background and

extracts the foreground using the basic background subtraction.

5. The extracted foreground can be used in activity recognition and tracking in

surveillance applications.

Input

Frames

Background

Model

Background

Subtraction

Estimated

background

Current frame

Foreground

Post

Processing

Results

Current frame

Background model construction and estimation

Proposed method simplifies foreground extraction from dynamic and cluttered

video scenes and shows better foreground extraction result than existing methods.

Motivation

1. Basic Background Subtraction technique does not work in real world

environment with dynamic and cluttered background scenes.

2. Adaptive background models are used to adapt with the dynamics of the real

world with pixel level classification as background and foreground.

3. A real-time background estimation model will simplify the foreground

extraction process by employing the basic background subtraction.

Frame 1

Frame 2

Frame t

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1

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ttT

tt XX

nttt eX

Gaussian Mixture Model (GMM)

for each pixel

Input frames

The sample video has been collected from http://www.gaitchallenge.org