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