Vision Based Traffic Surveillance System

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Presented By S.maheswaran

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Improvement of Pictorial Information• To improve the contrast of the image• To remove noise• To remove blurring caused by movement of the image acquisition devices• To correct geometrical distortions caused by the lens

Why Digital Image Processing

Automatic Machine perception for intelligent interpretation of scenes or pictures.

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Different Fields of Applications

• Character Recognition & Signature Verification

• Industrial Process Monitoring

• Biometrics and Forensic ( Recognition and Verification

of persons using Face, Palm & Fingerprints )

• Military surveillance and Target Identification

• Remote Sensing ( Satellite Image Processing)

• Safety and Security ( Night vision)

• Biomedical Engineering ( Diagnosis and Surgery )

•Traffic monitoring

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1. To monitor the road

2. To initiate automated vehicle tracking

3. Lane Jam Detection

4. To measure the number of vehicles in the lane

5. To recognize number plates of the vehicles

Need for Vision Based

Traffic Control

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1. Image Acquisition

2. Preprocessing

3. Feature Extraction

4. Morphological Processing

5. Decision

Methodology

Histogram Equalization

Median Filtering

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Image Acquisition

Initial Image Acquired from traffic lane

Original Image Gray Scale Image

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Histogram - plot between p(rk) vs rk

To enhance the contrast of the traffic image

n

nrp kkr )(

Histogram Equalization

Defined as mapping of gray levels p into gray levels q such that the distribution of gray level q is uniform

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Output of Histogram Equalization

Original Image Enhanced Image

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To reduce the noise in image, Median filtering is used. Provide excellent noise reduction.Median is calculated by first sorting all the pixel values from

the surrounding neighborhood into numerical order and then

replacing the pixel with middle pixel value.

Filtering

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Output of 3x3 Median Filtering

Enhanced image After 3X3 median filteringEnhanced image After 3X3 median filtering

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Segmentation - Subdivides an image into its constituent regions or objects.

Edge - A set of connected pixels whose two-dimensional first order derivative is greater than a specified threshold.

Edge Detection - 2-D gradient based approach is used. (Sobel Mask)

Segmentation

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-1 -2 -1

0 0 0

1 2 1

-1 0 1

-2 0 2

-1 0 1

Edge Detection Gradient Gx in X-direction

Gx = (z7+2z8+z9) – (z1+2z2+z3)

Gradient Gy in Y direction

Gy = (z3+2z6+z9) – (z1+2z4+z7)

Mask-1 Mask-2

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Gradient of an image f(x,y) at location (x,y) is defined as the vector

M = [G2x + G2y] ½

The direction of the gradient vector of an image f(x, y) at location (x,y) is given as

α(x,y) = tan-1[Gy / Gx]

Cont..

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Median filtered Image Edges of vehicle

Output of Edge Detection

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It deals with tools for extracting image components that are useful in the representation and description of region shape.

It has two main process,

1. Dilation

2. Region Filling

Morphological Processing

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Dilation This process helps to thicken the edges.

The output of dilation results in the foreground pixels are represented by 1's and background pixels by 0's.

3×3 square structuring element is taken for our work with the origin at its center.

1 1 1

1 1 1

1 1 1

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If an image denotes a subset containing pixels, whose elements are 8-connected boundary points of region beginning with a point p inside a boundary, the objective is to fill the entire image with 1's.

Dilated Image After Region Filtering

Region Filling

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Decision

120 sec (default time) > 95 5

100 sec 75 - 95 4

60 sec 50 - 75 3

45 sec 25 -50 2

30 sec < 25 1

Time AllottedNo of VehiclesS.No

The vehicles are counted after back ground elimination using certain algorithm. 1’s refers to vehicles and 0’s refers to free space. By counting number of 1’s we can find the number of vehicles in the lane.

Number of Vehicles is 5. So the time allotted for that lane is 30 sec.

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Conclusion

20 lane image were taken for analysis.

90% accuracy was obtained.

Time allotment to the channel is appropriate &

better than existing µc and sensor based system.

Recognition of number plates and speed of the vehicles is

possible.

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References 1. E. Atko¡ci unas1, R. Blake, A.Juozapavi¡cius, M.

Kazimianec, Nonlinear Analysis: Modelling and Control, 2005, Vol. 10, No. 4, 315–332 Image Processing in Road Traffic Analysis.

2. G.D. Sullivan, K. Baker, et al. Model-based Vehicle Detection and Classification using Orthographic Approximations, in: Proc. British Machine Vision AssociationConference, 1996.

3. D.A. Forsyth, J. Ponce. Computer Vision. A Modern Approach, Prentice Hall, 2003.

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4. D. Beymer, et al. Computer Vision System Measuring Traffic Parameters, in: Proc. IEEE Conf. On Computer Vision and Pattern Recognition,1977.

5. L.G. Shapiro, G.C. Stockman. Computer Vision, Prentice Hall, 2001.

6. L.A. B. Jähne, H. Haußecker, P. Geißler. Computer Vision and Applications, Academic Press, 1999.

Cont..

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