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VISION BASED INTELLIGENT

TRAFFIC MANAGEMENT SYSTEM

Authors:

MUHAMMAD FRAZ

FAISAL SAEED

M.HASSAN ASLAM

M.HASSAM MALHI

OWAIS JAVED

COMSATS INSTITUTE OF INFORMATION TECHNOLOGY, LAHORE

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OUTLINE

VISION BASED INTELLIGENT TRAFFIC MANAGEMENT

SYSTEM.

y Motivation

y

Introductiony Proposed Methodology

i. Vehicle Detection

ii. Density Estimation

iii. Traffic Management

y Results and Comparison

y Conclusion

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MOTIVATION

Conventional timing circuitry

y Time dependent

y Independent of traffic intensity

IR & sensors based systemy Costly

y Installation and maintenance problems

Vision Based Systemsy Real time processing difficulties

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INTRODUCTION

VISION BASED INTELLIGENT TRAFFIC MANAGEMENTSYSTEM.

y Dynamic Background Updating

y

Area of interesty Static thresh holding for gray scale to binary

conversion

y Traffic jam judgement

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PROPOSED METHODOLOGY

Vehicle Detection

y Pre-processing

y Background Subtraction

i. Dynamic background updating

bgn = (current_frame * v) + (bgn-1 * (1-v))a) Background Constant V

b) Background bg

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Fig: Dynamically updated background for background constant V

0.2,0.5 & 0.9 respectively

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PROPOSED METHODOLOGY

ii. Background Subtraction

iii. Morphological Operations

i. Erosion

ii. Dilation

iii. Filling holes

iv. Static Binary conversion

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Fig: Morphological operation results

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PROPOSED METHODOLOGY

Background Subtraction Flow chart

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Fig: Background subtraction flowchart

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PROPOSED METHODOLOGY

Density EstimationyArea of interest

y Rectangle Plotting

y Vehicle Counted

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Fig: Rectangle plotting and vehicle detection

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SYSTEM INITIALIZATION USING GUI

VISION BASED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM9Fig: Graphic user interface

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PROPOSED METHODOLOGY

Traffic Managementy Estimated Density

y Decision making

y Controlling Signals

VISION BASED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM10Fig: Traffic management Block diagram

Vision Based

AlgorithmMicroprocessor Traffic Signals

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PROPOSED METHODOLOGY

Signal Controlling

VISION BASED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM11Fig: Traffic management flowchart

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RESULTS AND COMPARISON

Algorithm tested on differentenvironmental condition videos

The size of video frames varies from

120x160 to 240x320Our algorithm use low frame size video to

increase the processing speed

http://www.youtube.com/user/mianhassanaslam?feature=mhsn#p/u

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RESULTS AND COMPARISON

Average vehicle detection accuracy is 92.30%

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Table: Detection results on videos

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RESULTS AND COMPARISON

Reference provided at the end

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Fig: Comparison bar chart

GuohuiZhang [1] Roya Red [2] Zhen Jia [3]

Our Method

91.5391

95.3

92.3

Detection Accuracy(%age)

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CONCLUSIONS

Traffic signal Handling on the basis of densityestimation

Operate in a real time scenario

No complex equipment requiredSwitching capability in case of vision based

system failure

Shortcomings such as Occlusions

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REFERENCES

[1] Grewal, M.S., Angus, P.A., 1993, Kalman filtering andpractice, Printince Hall, Upper Saddle River, NJ, USA.

[2] Guohui Zhang, Ryan P.Avery, Yinhai Wang, A Video

based vehicle detection and classification system forreal-time traffic data collection using uncalibrated video

camera,TRB Annual Meeting, 2007.

[3] P. Batavia, D. Pomerleau, and C. Thorpe, OvertakingVehicle Detection Using Implicit Optical Flow, Proc. IEEE

Transportation Systems Conf, 1997

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QUESTIONS

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VISION BASED INTELLIGENT TRAFFICMANAGEMENT SYSTEM