lauren zelinski engr 518 december 4 th ,2012

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Lauren Zelinski ENGR 518 December 4 th ,2012

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Lauren Zelinski ENGR 518 December 4 th ,2012. Purpose. Predicting traffic flow rates Measuring traffic density Increasing safety Enforcing traffic laws in intersections. Full setup of the intersection Wireless detection sensor Camera Video Strobe images. Length of Green Signal - PowerPoint PPT Presentation

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Page 1: Lauren  Zelinski ENGR 518 December 4 th ,2012

Lauren ZelinskiENGR 518

December 4th,2012

Page 2: Lauren  Zelinski ENGR 518 December 4 th ,2012

Predicting traffic flow rates Measuring traffic density Increasing safety Enforcing traffic laws in intersections

Purpose

Page 3: Lauren  Zelinski ENGR 518 December 4 th ,2012

Full setup of the intersection

•Wireless detection sensor

•Camera

•Video

•Strobe

•images

Page 4: Lauren  Zelinski ENGR 518 December 4 th ,2012

Length of Green Signal

Te-min=max(n*ts L/V)

N=number vehicle

ts=mean time headway in saturated traffic flow

V= mean speed

Page 5: Lauren  Zelinski ENGR 518 December 4 th ,2012

Traffic density vs speed

q=kvv=mean speedq=traffic flow ratek=traffic density

Page 6: Lauren  Zelinski ENGR 518 December 4 th ,2012

Traffic flow rate vs density

V=vf(1-k/kj)

Page 7: Lauren  Zelinski ENGR 518 December 4 th ,2012

IMAGE VERIFICATION AND CLARITY

Process digital image information by selectively modifying pixel intensity information in order to improve legibility or visibility of parts of a digital image.

How red light runners are caught and license plate numbers processed

Page 8: Lauren  Zelinski ENGR 518 December 4 th ,2012

Higher intensities around the license plate and headlights are made clearer as this 8-bit image is processed to a 4-bit image

Page 9: Lauren  Zelinski ENGR 518 December 4 th ,2012

Histogram of intensity of pixels in speed violation image

Algorithm for mapping 8-bit pixel intensity information to 4-bit focusing on higher intensity areas

Page 10: Lauren  Zelinski ENGR 518 December 4 th ,2012

Dynamic range of Camera

When processing digital images the camera needs to have a sufficient "dynamic range" to resolve all the light intensities under consideration.

The dynamic range of a CCD (Charge-coupled device) imaging system 30 is the ratio of CCD saturation (full well charge) to the read noise

The dynamic range is 45,000/11= 354091 levels or 20 Log 10(4091)=71 dB.

This produces a large volume of data. A 12 bit ADC produces 4096 levels, which in the case of an RGB CCD translates to 6.9x1010 bits of RGB information which are beyond the resolving capabilities of the human eye.

The average human eye can only resolve 64 or at most 128 grey levels or intensities.

Page 11: Lauren  Zelinski ENGR 518 December 4 th ,2012

Charge coupled device collects image and breaks into Red Green Blue analog streams which are then passed through an analog to digital converter

Passed to digital signal processor and filters 16-bit into 8-bit using intensity criteria described earlier and then converted to compact YUV and final the data compression algorithm is applied

Page 12: Lauren  Zelinski ENGR 518 December 4 th ,2012

4-bit version of earlier image after processes according to the algorithm

The higher intensity pixels around plate are now easier to read

Page 13: Lauren  Zelinski ENGR 518 December 4 th ,2012

References

•Andreassen D. (1995) A long term study of red light cameras and accidents, Report ARR 261, Australian Road Research Board, Vermont.

•Moran, M.A., Engelman, L., Fitzgerald, G., & Lynch, B. (1993) Polychotomous stepwise logistic regression. In BMDP Manual, Version 7

•US patent 5444442 sadakata et al.

•US patent 6240217 B1 Ercan et al.

Page 14: Lauren  Zelinski ENGR 518 December 4 th ,2012

Questions???