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Summary: BACKGROUND SUBTRACTION THROUGH MULTIPLE LIFE SPAN MODELING
Junliang Xing, Liwei Liu, Haizhou Ai
Computer Science and Technology Department, Tsinghua University, Beijing 100084, China
{xjl07,llw09}@mails.tsinghua.edu.cn, ahz@mail.tsinghua.edu.cn
ABSTRACT
Background subtraction plays a key role in many surveillance
systems. A good background subtractor should not only be
able to robustly detect targets under different situations (e.g.
moving and static), but also to adaptively maintain the back-
ground model against various influences (e.g. dynamic scenes
and noises). This paper proposes a novel background model-
ing approach with these good characteristics. By introducing
the "life span" concept into a background model, different
properties of the scene are obtained through different life span
models. Specifically, three different models, i.e., the Long
Life Span Model, the Middle Life Span Model, and the Short
Life Span Model, are online adaptively built and updated in
a collaborative manner. Output of the system gives an adap-
tive, robust, and efficient estimation of the foreground region
which can facility many practical applications. Experiment
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