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Traffic Sign Pattern Recognition

Traffic Sign Pattern Recognition. Pilho Kim (ECE), Zhaohua Wang, Yichang (James) Tsai (CE). CONTENTS. Introduction and Motivation Preliminary Works Build the project collaboration environment Construct the traffic sign database from MUTCD Reviews on related works using ANN

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Traffic Sign Pattern Recognition

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  1. Traffic Sign Pattern Recognition Pilho Kim (ECE), Zhaohua Wang, Yichang (James) Tsai (CE)

  2. 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

  3. 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.”

  4. Preliminary Works:Build the project collaboration environment • TSPR Project Wiki:http://www.pilhokim.com/project/signpattern/signwiki/

  5. 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)

  6. 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)

  7. Preliminary Works:Search proper image abstractions for sign recognition • Devise the feature correlation graph (FCG) Template matching Canny edge X-Y profile

  8. Preliminary Works:Develop the ANN modules • Choose the proper ANN engine

  9. PROPOSED PLAN • Closed convex polygon detectionfor the accurate traffic sign boundary detection • Color-coded line receptors as image features

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

  11. Proposed Approaches:Color-coded line receptors as image features for ANN • 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. Line Receptors Line Encoding Example

  12. 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

  13. Appreciate Your Attention

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