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Traffic Sign Localization & Recognition Using

A Client-Server ArchitectureAt

GTU PG School , BISAG Campus, GandhinagarCDAC-GTU-BISAG ME Program

IEEE Topic Presentation

PRESENTATION ON

By : KISHAN PATEL M.E ITSNS

14th October 2016

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CONTENTS Introduction Challenges to overcome Solution Conclusion References

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INTRODUCTION Traffic Sign Localization and recognition

system using Network fundamentals e.g. (Client and Server)

Identification and recognition of Traffic Signs (TSs) is an area of great interest in “Intelligent Transportation Systems”

Over the last decade, numerous camera-based platforms have been developed to detect and recognize traffic signs.

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CHALLENGES Non-appearance of signs

as a result of unpleasant weather conditions, as shown in Figure

The detection of TSs in nighttime conditions imposes additional challenges that render detection and recognition more difficult.

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CHALLENGES To overcome these problems, several

solutions and architectures have been developed.

All these architectures are based on the detection of TSs using cameras and image processing techniques which might fail in detecting signs as shown in Figure

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SOLUTION We achieve this goal by adopting a Client-

Server architecture instead of using cameras. Clients, represented by vehicles in our

architecture, contain GPS devices that are used to determine their geographic position in the map.

Server is a powerful computer dedicated to store important information related to all traffic signs within a given city, and to manage requests from/to vehicles.

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HOW IT’S WORKS? Client(vehicle) send

request to server Request contains

Geographical position of client e.g. Longitude & Latitude

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HOW IT’S WORKS? Server keep all the traffic sign information in

city including: Traffic sign Position (Longitude & Latitude) Street name Brief description of the content of traffic sign

(e.g. speed limit 60 km/h) This system’s only drawback is that the database

should be updated frequently.

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WHICH INFORMATION SHOULD BE STORE IN DB?1. Traffic Sign Information

TsContent: This field contains information carried out by TSs such as, YIELD, STOP, Speed Limit information, etc.

StreetName1: It represents the name of the street where the sign is mounted.

StreetName2: It represents the name of the second street if the TS is located at an intersection of two named roads.

isAtIntersection: This Boolean field indicates the value TRUE if the TS is located at an intersection of two named roads.

isOneWay: If this Boolean value is TRUE, both TSs situated on the right and left of the current lane are taken into consideration by the travelling vehicle.

Longitude: This value represents the longitude of the traffic sign, Latitude: This information indicates the latitude of the traffic sign.

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WHICH INFORMATION SHOULD BE STORE IN DB?2. Car Information

StreetName1: This field represents the name of the street where the car is travelling;

isOneWay: If this boolean value is TRUE, both TSs situated on the right and left of the current lane are taken into consideration by the travelling vehicle.

Longitude: This value represents the longitude position of the car.

Latitude: This value indicates the latitude position of the ca

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PROBLEM ARRIVES AT COMMUNICATION..

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FILTERING ALGORITHM1. Load longitude and latitude values of the Vehicle and

the TS Calculate the Distance between Vehicle and TSs

if Distance ≤ threshold then push TS information into signQueues. else Increase the threshold Goto: 1 end if

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FILTERING ALGORITHM2. Load full TS information of the selected sign

if Sign.StreetName1 = Car.StreetName1 then Take the sign as result. else if Sign.isAtIntersection!=TRUE then Pop this TS from sign vector Goto: 2 else update Vehicle information until next time instance end if end if

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FILTERING ALGORITHMif Car.StreetName1==Sign.StreetName2 then Take the sign as result. else Pop this TS from sign vector and

Goto: 2 end if

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EXPERIMENT AND RESULT ANALYSIS We constructed a local dataset related to down-

town Ottawa, which contains all information of traffic signs.

We drive our car along a given street (for instance LAURIER Street).

This system calculates the distance between the vehicle and appearing signs every 30 ms.

When the distance between the vehicle and signs is smaller than a threshold, a warning message is then sent to the driver.

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EXPERIMENT AND RESULT ANALYSIS

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EXPERIMENT AND RESULT ANALYSIS

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COMPARISON WITH VISION-BASED METHODS Histogram of Oriented

Gradients (HOG) Maximally Stable

Extremal Regions (MSER)

Here the result can be seen clearly that this system is more faster then above two method.

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CONCLUSION Traffic signs that fulfill the requirement of

vehicles are selected and communicated to drivers using this method.

We have compared this architecture to two vision-based systems, and found that our system performs better than them.

This architecture is easy to impalement as compare to another.

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REFERENCES OF IEEE PAPER[1] Abdelhamid Mammeri, Azzedine Boukerche and Jingwen Feng “Traffic Signs Localization and Recognition Using A qClient-Server Architecture” Wireless Communications and Networking Conference (WCNC), 2016 IEEE

Abdelhamid Mammeri Jingwen Feng Azzedine Boukerche

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