A foreground detection system for automatic surveillance

Automated surveillance has long been an application goal of computer vision. An integral part of such surveillance systems is concerned with accurately segmenting foreground objects from the static background in the videos. In this thesis we introduce a novel system for background subtraction, which takes a di erent approach than the conventional background subtraction systems. We make the assumption that the video background is stationary and the foreground objects take up only a small portion of the entire frame at any given time. This speci c assumption allows us to formulate the foreground signal as a sparse additive error introduced to otherwise clean background signal. We outline the algorithm for performing background subtraction using linear programming, and demonstrate accurate segmentations of foreground objects under realistic surveillance scenarios. The proposed method is on par with the state of the art approaches for accurately segmenting the foreground under challenging conditions. Furthermore we propose several methods for building a set of bases to represent the background and provide empirical justi cation of their e ectiveness.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Dikmen, Mert

Contributors dc:contributor

• Huang, Thomas S.

• computer vision
• background subtraction
• sparse representation
• linear decoding
• automatic surveillance

Statement dc:rights

• Copyright 2009 Mert Dikmen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/14760

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/14760