Modeling, Design and Analysis of Intelligent Traffic Control System Based on Integrated Statistical Image Processing Techniques

Yasar Abbas Ur RehmanDepartment of Electrical EngineeringCity University PeshawarFAST, NUCES Peshawar Campus

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Outline•Introduction•Problem Statement•Proposed Solution•Results •Conclusion

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Introduction•Monitoring and control of intercity traffic

▫Not a trivial problem▫Careful planning▫Increase in road infrastructure?▫Availability of Technological Assets

•Camera controlled monitoring and control▫Vehicle flow▫Speed calculation▫Automatic Number Plate Recognition (ANPR)▫Crash detection

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Problem Statement•Detection Problem

▫Presence and absence of vehicle▫No background information

•Design of Autonomous System for Traffic▫Detection ▫Classification▫Display

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Proposed Solution•Detection Problem

▫Background Modeling

(1)

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Vehicle Detection

(2)

(3)

  (4) 

(5) To simplify calculations, we make a use of histogram and relate it with the above equations: 

(6) 

(7)

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Cont. (8)

  (9)

  (10)

(11)

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System Design •Vehicle Detection System (VDS)•Vehicle Counting and Classification

System (VCCS)•Traffic Signals Control System (TSCS)•Data Display System (DDS)

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Cont.•System Interconnection

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VDS• Probability Based Vehicle Detection (PBVD)

Algorithm▫ Frame acquisition▫ Vehicle appearance

(10)

(11)

▫ Vehicle extraction▫ Maximum area calculation

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VCCS•Vehicle labeling•Classification of vehicles

▫Small▫Medium▫Large

•Send vehicle statistics to ▫TSCS▫DDS

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TSCS•Vehicle comparison for both lanes•If same number of vehicles

▫Assign equal timing to both lanes

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DDS•Display total number of vehicles•Number of pixels each vehicle contain•Vehicle category•Green signal for road

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Experimental Results•Background update parameter

▫α = 1 × 10-3

•Threshold for vehicle appearance▫t = 300

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Cont..

Prototype of Proposed System

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Cont.

Data Display System (DDS)

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Cont.

a) Image of empty road b) image of the road containing car c) image enhancement after median filtering d) image after frame subtraction

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Cont.

a) Image after edge detection b) Binary dilation c) Binary Hole Filling d) Binary Erosion

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Cont.

a) Area Calculation b) Final Result (Detected Objects)

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Cont.

Current frame

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Cont.

Frame subtraction

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Cont.

Edge detection and binary dilation

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Cont.

Resultant image after binary hole filling,

erosion and area calculation

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Cont.

Final result

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Cont..

0 50 100 150 200 2500

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Pixel intensities

Abs

olut

e di

ffer

ene

Spred after absolute histogram subtraction

Spread after absolute histogram subtraction

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Conclusion•Traffic control system for

▫Monitoring▫Control of intercity traffic

•Prototype testing▫Accurate results

•Real time testing▫Satisfactory results▫Morphological operators need control▫Need of restoration techniques

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Question