Shadow detection for improved object tracking in surveillance cameras
(2013) In Master's Theses in Mathematical Sciences FMA820 20131Mathematics (Faculty of Engineering)
- Abstract
- Object tracking algorithms and motion triggered alarms are often dis-
turbed by shadows. It is challenging to separate between moving objects
and shadows since they have similar movement patterns and image prop-
erties. In this thesis, three different approaches to detect shadows are
developed and evaluated. The identified shadows determine what parts
of the image not to track and what alarms to ignore.
The first approach utilizes a mathematical model to estimate the intensity
attenuation of a shadowed region. The second approach applies thresh-
olding to identify shadows based on information about the attenuation,
color change and texture preservation. The third approach makes use of
probability distributions describing... (More) - Object tracking algorithms and motion triggered alarms are often dis-
turbed by shadows. It is challenging to separate between moving objects
and shadows since they have similar movement patterns and image prop-
erties. In this thesis, three different approaches to detect shadows are
developed and evaluated. The identified shadows determine what parts
of the image not to track and what alarms to ignore.
The first approach utilizes a mathematical model to estimate the intensity
attenuation of a shadowed region. The second approach applies thresh-
olding to identify shadows based on information about the attenuation,
color change and texture preservation. The third approach makes use of
probability distributions describing shadows, background and objects. An
energy minimization method using discrete optimization is then used in
order to classify the pixels as shadow, object or background. All three
approaches were evaluated using several different image sequences with
corresponding ground truth.
Deriving a shadow detection algorithm that is independent of environ-
ment and type of objects in the scene turned out to be the major chal-
lenge of this thesis. The best result, a true positive rate of 65.5% and a
false positive rate of 2.2%, was achieved with the second approach apply-
ing intensity, chromaticity and texture. However, there is still a trade-off
between the shadow detection and object discrimination. To further im-
prove the performance, more features and a more extensive data set could
be useful. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/3813265
- author
- Berg, Katarina LU and Hellström Håkansson, Jonna LU
- supervisor
-
- Karl Åström LU
- organization
- course
- FMA820 20131
- year
- 2013
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3250-2013
- ISSN
- 1404-6342
- other publication id
- 2013:E34
- language
- English
- id
- 3813265
- date added to LUP
- 2013-09-20 12:20:33
- date last changed
- 2015-12-01 13:38:28
@misc{3813265,
abstract = {{Object tracking algorithms and motion triggered alarms are often dis-
turbed by shadows. It is challenging to separate between moving objects
and shadows since they have similar movement patterns and image prop-
erties. In this thesis, three different approaches to detect shadows are
developed and evaluated. The identified shadows determine what parts
of the image not to track and what alarms to ignore.
The first approach utilizes a mathematical model to estimate the intensity
attenuation of a shadowed region. The second approach applies thresh-
olding to identify shadows based on information about the attenuation,
color change and texture preservation. The third approach makes use of
probability distributions describing shadows, background and objects. An
energy minimization method using discrete optimization is then used in
order to classify the pixels as shadow, object or background. All three
approaches were evaluated using several different image sequences with
corresponding ground truth.
Deriving a shadow detection algorithm that is independent of environ-
ment and type of objects in the scene turned out to be the major chal-
lenge of this thesis. The best result, a true positive rate of 65.5% and a
false positive rate of 2.2%, was achieved with the second approach apply-
ing intensity, chromaticity and texture. However, there is still a trade-off
between the shadow detection and object discrimination. To further im-
prove the performance, more features and a more extensive data set could
be useful.}},
author = {{Berg, Katarina and Hellström Håkansson, Jonna}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Shadow detection for improved object tracking in surveillance cameras}},
year = {{2013}},
}