Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, 2007...

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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 ZengAdvisor : Dr. Yen – Ting ChenDate : 2013.11.20

Fast Detection of Vehicles Based-on the Moving Region

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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 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 ofcoordinate (x, y) at time instant k is , at time instant k+1 is , and their luminance rate is

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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 and 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)

Figure 4. The background updating

After the algorithm, construct the background model and this model can updating through self-adaptive.

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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