Improved Gaussian Mixtures for Robust Object Detection by

Adaptive Multi-Background Generation 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 Modelling

Implemented System

Frame 1

Frame 2

Frame t

..

Ki titittit XwXP

1 ,,, ),,()(

)()(2

1

2/12/,,

1

||)2(

1)(

ttT

tt XX

nttt eX

Gaussian Mixture Model (GMM)

for each pixel

Input scenes

A pixel model is constructed and updated for each pixel which maintains a

mixture of Gaussian distributions for modelling multi-modal distribution

caused by moving foregrounds and repetitive background motions [1-3].

Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. However,

object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground

data proportion, and instability with varying operating environments. This paper presents an effective technique to

eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to

detect objects from one or more believe-to-be backgrounds.

The images shown in the header has been taken

from http://www.informationliberation.com

[1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background Generation Technique using

Gaussian Mixture Models for Robust Object Detection, IEEE International Conference On Advanced Video and Signal Based

Surveillance (AVSS), New Mexico, USA, 2008.

[2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, A Hybrid Object Detection Technique from Dynamic

Background Using Gaussian Mixture Models, IEEE International Workshop on Multimedia Signal Processing (MMSP), Cairns,

Australia, 2008.

[3] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for Robust Object Detection by

Adaptive Multi-Background Generation, International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008.

Quantitative Evaluation

First

Frame

Test

Frame

Ideal

Result

Lee’s

Tech.

Proposed

Tech.

P(x)

Existing Models

Intensity

New Model

New Model Induction Scheme 3

Experimental results on 14 test

sequences including PETS and

Wallflower datasets.

Error rates at medium learning

rate (α = 0.01) and the standard

deviation of the error rates over

three learning rates α = 0.1, α =

0.01, and α = 0.001.

1

2

8

6

Proposed Detection Scheme 4

Qualitative Evaluation 7

Model Quality Visualisation 5

One model

Two models

More than two models

0 127 255

Input Frames

Visualisation

Visual comparison results at medium learning rate, α = 0.01. Model matching:

B/G Model Selection:

F/G Detection:

;

Model distance