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Fast Detection of Vehicles
Based-on the Moving Region
Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG
Computer-Aided Design and Computer Graphics, 2007 10th IEEE
International Conference on, p 202 - 207
Presenter : Jia – Hong Zeng
Advisor : Dr. Yen – Ting Chen
Date :
2013.11.20
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OUTLINE
 Introduction
 Purpose
 Materials and Methods
 Results
 Conclusion
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INTRODUCTION(1/5)
 The precise location and tracking of the
moving vehicles is an important part in
Intelligent Transportation System(ITS), and it
still encounters many difficulties, such as :
 Shadows
 Camera noises in real-world traffic scenes
 Changes in lighting
 Weather conditions, etc.
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INTRODUCTION(2/5)
 The shadows will influence the vehicle
detection process and cause great inaccuracy
even error in the following process such as
 Precise positioning
 Extraction of key part
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INTRODUCTION(3/5)
 In the area of motion detection and tracking,
most researches have been devoted to
solving several problems :
 Background extraction
 Background updating
 Shadow elimination
 Edge detection
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INTRODUCTION(4/5)
 Shadow detection technique can be classified
into two groups :
 Property-based
• Geometry
• Brightness
• Color
 Model-based
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INTRODUCTION(5/5)
 The main process is done in three steps :
 Moving region detection
 Shadow detection
 Edge detection
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PURPOSE
This paper presents a new method for detecting
vehicles.
 Self-adaptive background
 Shadow detection fast
 Eliminate the shadow
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MATERIALS AND METHODS(1/11)
The system proposed
for the shadow detection
of moving objects
consists of two parts :
 Pretreatment
 Exact Vehicle Detection
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MATERIALS AND METHODS(2/11)
 Adaptive threshold on Gaussian model
The mixture probability density function of the
difference model 𝐼𝐷 is
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MATERIALS AND METHODS(3/11)
Let
be the
histogram probability of a difference value(d).
The threshold value e is determined by a fitting
criterion defined as:
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MATERIALS AND METHODS(4/11)
 Quick self-adaptive background updating
 Extract Background Model(BM) using
the selective averaging method.
 Extract Moving Region(MR) with the
binary object mask image.
 Take the difference between Current
Image(CI) and Current Background(CB)
image to determine which area should be
updated or not.
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MATERIALS AND METHODS(5/11)
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MATERIALS AND METHODS(6/11)
 Constructing the Background Model(BM)
The experiment demonstrates that letting n be
100 can bring a good result.
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MATERIALS AND METHODS(7/11)
 Acquiring the Moving Region(MR)
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MATERIALS AND METHODS(8/11)
 Self-adaptive background updating
α is a weight factor, assigned to the current and
instantaneous background. The weight has
been empirically determined to be 0.1 can give
the best result of experiment.
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MATERIALS AND METHODS(9/11)
 Shadow detection based on improved HSV
color space approach
ρ(x, y) is the reflectance of the object surface,
and E(x, y) is the irradiance and is computed as
follows:
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MATERIALS AND METHODS(10/11)
Assume that the luminance of a certain point of
coordinate (x, y) at time instant k is 𝑆𝑘 (𝑥, 𝑦), at
time instant k+1 is 𝑆𝑘+1 (𝑥, 𝑦), and their
luminance rate is 𝑅𝑘 (𝑥, 𝑦)
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MATERIALS AND METHODS(11/11)
 Detection of the shadows
RGB value of the pixel in the image of the
current frame and the background image
obtained by updating the image of the former
frame is represented as 𝐼𝑘 (𝑖, 𝑗)and 𝐵𝑘 (𝑖, 𝑗)
respectively.
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RESULTS(1/5)
The algorithm presented in this paper has a very good
effect in recognizing the certain parts especially those
adjacent to the shadows.
Figure 3. Two raw images from the sequences
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RESULTS(2/5)
After the algorithm, construct the background model
and this model can updating through self-adaptive.
Figure 4. The background updating
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RESULTS(3/5)
Figure 5. The edge of the moving region
Figure 6. The edges of the shadow
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RESULTS(4/5)
Figure 7. The subtraction of the edge of the moving region
without the shadow
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RESULTS(5/5)
Figure 8. The detection of the exact vehicle in the real image
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CONCLUSION
 Presented a new and fast vehicle detecting system
capable of robustly working under most
circumstances.
 The system is general enough to be capable of
detecting and classifying vehicles while requiring
only minimal scene-specific parameters, which can
be obtained through training.
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CONCLUSION
 The exact detection of the vehicle object makes the
location of the key part of the vehicle possible,
which also found bases for the following steps
such as classification or tracking of the vehicles.
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THANKS FOR YOUR ATTENTION