Traffic Sign Pattern Recognition

Pilho Kim (ECE), Zhaohua Wang, Yichang (James) Tsai (CE)

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CONTENTS Introduction and Motivation Preliminary Works

Build the project collaboration environment Construct the traffic sign database from MUTCD Reviews on related works using ANN Search proper image abstractions for sign recognition Develop the ANN modules

Proposed Approaches Closed convex polygon detection algorithms for precise

traffic sign region extraction Color-coded line receptors as image features for ANN

Concluding Remarks

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Introduction and Motivation

Traffic asset management High demands on the efficient and cost-saving traffic asset

management system

Safe driving and autonomous land vehicle (ALV) Driver assistant system can reduce car accidents to save lives of

drivers ALV should have this technology to make it practical

So we need “Automatic geographical traffic sign location and type logging system using computer vision.”

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Preliminary Works:Build the project collaboration environment TSPR Project Wiki:

http://www.pilhokim.com/project/signpattern/signwiki/

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Preliminary Works:Construct the traffic sign database from MUTCD

Build the traffic sign database from MUTCD (Manual on uniform traffic control devices)

Prepare two sets for the computation and the database (MySQL)

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Preliminary Works:Construct the traffic sign database from MUTCD

Reviews on related works using ANN

http://www.citeulike.org/user/pilho/tag/sign

Fang et al. (2004)

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Preliminary Works:Search proper image abstractions for sign recognition

Devise the feature correlation graph (FCG)

Template matching Canny edge X-Y profile

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Preliminary Works:Develop the ANN modules

Choose the proper ANN engine

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PROPOSED PLAN

Closed convex polygon detectionfor the accurate traffic sign boundary detection

Color-coded line receptors as image features

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Proposed Approaches:Closed convex polygon detection algorithms for precise traffic sign region extraction

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Proposed Approaches:Color-coded line receptors as image features for ANN

Line Receptors Line Encoding Example

Enhance above simple image pattern recognition algorithms to : Count on the image color and line crossing features by introducing

introduce the multi-level encoding schema Improve the existing inner and outer entropy computing methods.

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Concluding Remarks for future investigation

Traffic sign pattern recognition in the real scene capture is very challenging

Finding the proper robust features for the ANN training is the key to solve the problem

Multi-level image processing and recursive result enhancements are required.

Understanding the context of image capturing environment will give clues for recognition

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Appreciate Your Attention