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V IDEO OBJECT SEGMENTATION AND
ITS SALIENT MOTION DETECTION
USING ADAPTIVE BACKGROUND GENERATION
Kim, T.K.; Im, J.H.; Paik, J.K.;
Electronics Letters
Volume: 45 , Issue: 11
Digital Object Identifier: 10.1049/el.2009.0663
Publication Year: 2009 , Page(s): 542 - 543
IET JOURNALS
Ming-Yuan Shieh
M9820202
Chung-Chieh Lien
1
2010/10/6
O UTLINE
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
Abstract

Introduction

Background modelling and updating

Video object segmentation using proposed
algorithm

Experimental results

Conclusion

References
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A BSTRACT
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
Video object segmentation often fails when the
background and foreground contain a similar
distribution of colours.

Proposed is a novel image segmentation
algorithm to detect salient motion under a
complex environment by combining temporal
difference and background generation.

Experimental results show that the proposed
algorithm provides a twice higher matching ratio
than the conventional Gaussian mixture-based
approaches under various conditions.
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I NTRODUCTION -1
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
Background generation [1] has been an early
criterion for video object segmentation, while
foreground modelling has recently been used in
conjunction with background modelling for more
accurate movement detection.

Recently, the mixture of the Gaussian method is
becoming popular because it can deal with slow
lighting changes, periodical motions from the
cluttered background, slow moving objects,
long-term scene changes, and camera noise.
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I NTRODUCTION -2
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
In spite of the above-mentioned advantages, it
cannot adapt to the quick lighting changes and
cannot successfully handle shadows.

In this Letter, we present a real-time robust
method that provides a realistic foreground
segmentation to detect salient motions in
complex environments by combining temporal
difference and background generation.

The proposed method, shown Fig. 1, aims at
performing real-time background generation
and salient motion detection of moving objects.
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I NTRODUCTION -3
Fig. 1 Proposed video object segmentation algorithm
using adaptive background generation 2010/10/6
B ACKGROUND MODELLING
AND UPDATING -1
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
As the first step, we estimate the optical flow
between two images
and
by
minimising the Euclidean distance defined as:
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B ACKGROUND MODELLING
AND UPDATING -2
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
For each pixel
in
, where
represents the displacement of the pixel at
and is initially set to be zero as:
where w represents the neighbouring displacement. 2010/10/6
B ACKGROUND MODELLING
AND UPDATING -3
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
If E is smaller than a pre-specified threshold, the
background is updated at the corresponding w.

In the experiment we have used 0.35 for the
threshold value.

For a w with high E value the background is
generated by minimising E, while the median
filter is used for the remaining w.
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B ACKGROUND MODELLING
AND UPDATING -4
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
To overcome the drawbacks of a median filter under
dynamic conditions, it is necessary to keep updating
the background expressed as

where
represents the background at time
t,
the input image at time t, and
the mixing
ratio in the range [0, 1].

To detect an object’s salient motion in the background,
we use the initial background from the previous
frame
.
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B ACKGROUND MODELLING
AND UPDATING -5
Fig. 2 Results of background generation using proposed algorithm
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V IDEO OBJECT SEGMENTATION
USING PROPOSED ALGORITHM -1
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
Temporally adjacent images
and
are
subtracted and a threshold is applied to the difference
image for extracting the entire region of change.

To detect the slow motion or static objects, a fixed
weighted accumulation is used to compute the
temporal difference image
as:
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V IDEO OBJECT SEGMENTATION
USING PROPOSED ALGORITHM -2
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
Where is the weighting parameter which describes
the temporal range for accumulating difference
images.
is initialised to an empty image. In
this Letter, we set T = 20 and = 0.5 for all
experiments.

We assume that the foreground with salient motion
shows consistency over a period of time in both
temporal difference and background subtraction.
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V IDEO OBJECT SEGMENTATION
USING PROPOSED ALGORITHM -3
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
It means that the optical flow of the region with
salient motion in the given time period
should be in the same direction.

The salient motion is detected using the temporal
difference with background subtraction, along with
the change in illumination.

On the other hand, simple background subtraction
exhibits inaccurate results.

The output of salient motion detection is obtained as:
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V IDEO OBJECT SEGMENTATION
USING PROPOSED ALGORITHM -4
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
Where
represents the generated background
image by using the proposed algorithm. In this Letter,
the difference between
and
is computed,
and the difference image is then the threshold for
obtaining the change in motion.

Fig. 3 represents the temporal differences, subtracted
background images, and the detected salient motion
regions.
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Results of foreground segmentation using proposed algorithm
a–d: Temporal difference images at 425th, 500th, 551st, 651st frames
e–h: Subtracted background images at 425th, 500th, 551st, 651st frames
i – l: Detected salient motion regions at 425th, 500th, 551st, 651st frames
Fig. 3
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E XPERIMENTAL
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RESULTS -1

The proposed algorithm was tested and compared to
conventional methods using both simulated and real
video sequences.

For evaluating the performance of the proposed
algorithm, we compared the segmentation matching
ratios of the proposed and Gaussian mixturebased
algorithms [7]. The matching ratio pixel is defined as:
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E XPERIMENTAL
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RESULTS -2

where
represent the sum of pixels
inside the boundaries for manually specified
foreground segmentation in the original image,
and
represent the sum of pixels
given by the proposed method.

Fig. 4 shows the graph of matching ratios.
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E XPERIMENTAL
RESULTS -3
Fig. 4 Comparison of segmentation matching ratio to ground truth
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C ONCLUSION
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
We propose a novel video object segmentation
and salient motion detection algorithms using
background generation to cope with problems in a
complicated, unstable background.

Experimental results show that the proposed
background generation with the salient motion
detection approach works twice as accurately as
the conventional Gaussian mixture-based method.
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R EFERENCES
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1)
Mittal, A., and Paragios, N.: ‘Motion-based background subtraction using adaptive
kernel density estimation’. IEEE Conf. Computer Vision and Pattern Recognition,
Washington, DC, USA, July 2004, pp. 302–309
2)
Wildes, R.P.: ‘A measure of motion salience for surveillance application’. IEEE Conf.
Image Processing, Chicago, IL USA, October 1998, pp. 183–187
3)
Wixson, L.: ‘Detecting salient motion by accumulating directionally flow’, IEEE Trans.
Pattern Anal. Mach. Intell, 2000, 22, (8), pp. 774–779
4)
Monnet, A., Mittal, A., Paragios, M., and Ramesh, V.: ‘Background modeling and
subtraction of dynamic scenes’. IEEE Proc. Computer Vision, Beijing, China, October
2003, pp. 1305–1312
5)
Velastin, S., and Davies, A.: ‘Intelligent CCTV surveillance: advances and limitations’.
Proc. Methods, Techniques, Behavioral Research, Wageningen, The Netherlands, 2006
6)
Ren, Y., Chua, C., and Ho, Y.: ‘Motion detection with non-stationary background’. Proc.
Image Analysis and Processing, Palermo, Italy,September 2001, pp. 78–83
7)
Stauffer, C., and Grimson, W.E.L.: ‘Adaptive background mixture models for real-time
tracking’. IEEE Proc. Computer Vision and Pattern Recognition, Fort Collins, CO, USA,
June 1999, pp. 246–252
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