Modelling of the background (“uninteresting parts of the scene”), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detectin

and compare the performance of our method with that of five state-of-the-art background modelling methods.

A brief description of the Wallflower image sequences follows:

Moved Object (MO): A person enters into a room, makes a phone call, and leaves. The

phone and the chair are left in a different position.

Time of Day (TOD): The light in a room gradually changes from dark to bright. Then, a

person enters into the room and sits down.

Light Switch (LS): A room scene begins with the lights on. Then a person enters the

room and turns off the lights for a long period. Later, a person walks into the room, switches on the light, and moves the chair, while the door is closed. The camera sees the room with lights both on and off during the training stage.

Waving Trees (WT): A tree is swaying and a person walks in front of the tree.

Camouflage (C): A person walks in front of a monitor, which has rolling interference bars

on the screen. The bars include color similar to the person’s clothing.

Bootstrapping (B): The image sequence shows a busy cafeteria and each frame contains

people.

Foreground Aperture (FA): A person with uniformly colored shirt wakes up and begins

to move slowly.

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