Vision Based Traffic Surveillance System

2,292 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
2,292
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
158
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Vision Based Traffic Surveillance System

  1. 1. Vision Based Traffic Surveillance System Presented By S.maheswaran
  2. 2. <ul><li>Improvement of Pictorial Information </li></ul><ul><li>To improve the contrast of the image </li></ul><ul><li>To remove noise </li></ul><ul><li>To remove blurring caused by movement of the </li></ul><ul><li>image acquisition devices </li></ul><ul><li>To correct geometrical distortions caused by </li></ul><ul><li>the lens </li></ul>Why Digital Image Processing <ul><li>Automatic Machine perception for intelligent interpretation of scenes or pictures. </li></ul>
  3. 3. Different Fields of Applications <ul><li>Character Recognition & Signature Verification </li></ul><ul><li>Industrial Process Monitoring </li></ul><ul><li>Biometrics and Forensic ( Recognition and Verification </li></ul><ul><li>of persons using Face, Palm & Fingerprints ) </li></ul><ul><li>Military surveillance and Target Identification </li></ul><ul><li>Remote Sensing ( Satellite Image Processing) </li></ul><ul><li>Safety and Security ( Night vision) </li></ul><ul><li>Biomedical Engineering ( Diagnosis and Surgery ) </li></ul><ul><li>Traffic monitoring </li></ul>
  4. 4. <ul><li>To monitor the road </li></ul><ul><li>To initiate automated vehicle tracking </li></ul><ul><li>Lane Jam Detection </li></ul><ul><li>To measure the number of vehicles in the lane </li></ul><ul><li>To recognize number plates of the vehicles </li></ul>Need for Vision Based Traffic Control
  5. 5. <ul><li>Image Acquisition </li></ul><ul><li>Preprocessing </li></ul><ul><li>Feature Extraction </li></ul><ul><li>Morphological Processing </li></ul><ul><li>Decision </li></ul>Methodology Histogram Equalization Median Filtering
  6. 6. Image Acquisition <ul><li>Initial Image Acquired from traffic lane </li></ul>Original Image Gray Scale Image
  7. 7. <ul><li>Histogram - plot between p(r k ) vs r k </li></ul><ul><li>To enhance the contrast of the traffic image </li></ul>Histogram Equalization <ul><li>Defined as mapping of gray levels p into gray levels q such that the distribution of gray level q is uniform </li></ul>
  8. 8. Output of Histogram Equalization Original Image Enhanced Image
  9. 9. <ul><li>To reduce the noise in image, Median filtering is used. </li></ul><ul><li>Provide excellent noise reduction. </li></ul><ul><li>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. </li></ul>Filtering
  10. 10. Output of 3x3 Median Filtering Enhanced image After 3X3 median filtering
  11. 11. 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
  12. 12. Edge Detection <ul><li>Gradient G x in X-direction </li></ul><ul><li>G x = (z 7 +2z 8 +z 9 ) – (z 1 +2z 2 +z 3 ) </li></ul><ul><li>Gradient G y in Y direction </li></ul><ul><li>G y = (z 3 +2z 6 +z 9 ) – (z 1 +2z 4 +z 7 ) </li></ul>Mask-1 Mask-2 -1 -2 -1 0 0 0 1 2 1 -1 0 1 -2 0 2 -1 0 1
  13. 13. <ul><li>Gradient of an image f(x,y) at location (x,y) is defined as the vector </li></ul><ul><li>M = [G 2 x + G 2 y] ½ </li></ul><ul><li>The direction of the gradient vector of an image f(x, y) at location (x,y) is given as </li></ul><ul><li>α (x,y) = tan -1 [Gy / Gx] </li></ul>Cont..
  14. 14. Median filtered Image Edges of vehicle Output of Edge Detection
  15. 15. <ul><li>It deals with tools for extracting image components that are useful in the representation and description of region shape. </li></ul><ul><li>It has two main process, </li></ul><ul><li>1. Dilation </li></ul><ul><li>2. Region Filling </li></ul>Morphological Processing
  16. 16. Dilation <ul><li>This process helps to thicken the edges. </li></ul><ul><li>The output of dilation results in the foreground pixels are represented by 1's and background pixels by 0's. </li></ul><ul><li>3×3 square structuring element is taken for our work with the origin at its center. </li></ul>1 1 1 1 1 1 1 1 1
  17. 17. <ul><li>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. </li></ul>Dilated Image After Region Filtering Region Filling
  18. 18. Decision <ul><li>The vehicles are counted after back ground elimination using certain algorithm. </li></ul><ul><li>1’s refers to vehicles and 0’s refers to free space. </li></ul><ul><li>By counting number of 1’s we can find the number of vehicles in the lane. </li></ul><ul><li>Number of Vehicles is 5 . So the time allotted for that lane is 30 sec. </li></ul>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 Allotted No of Vehicles S.No
  19. 19. Conclusion <ul><li>20 lane image were taken for analysis. </li></ul><ul><li>90% accuracy was obtained. </li></ul><ul><li>Time allotment to the channel is appropriate & </li></ul><ul><li>better than existing µ c and sensor based system. </li></ul><ul><li>Recognition of number plates and speed of the vehicles is </li></ul><ul><li>possible. </li></ul>
  20. 20. References <ul><li>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. </li></ul><ul><li>2. G.D. Sullivan, K. Baker, et al. Model-based Vehicle Detection and Classification using Orthographic Approximations , in: Proc. British Machine Vision AssociationConference, 1996. </li></ul><ul><li>3. D.A. Forsyth, J. Ponce. Computer Vision. A Modern Approach , Prentice Hall, 2003. </li></ul>
  21. 21. 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..
  22. 22. Open For Discussion
  23. 23. Thank you

×