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  1. Intelligent Parking System: Parking Guide Application in Beijing and Method for License Plate Localization Chen, Hao Department of Transportation Engineering, Beijing Institute of Technology, Beijing, PR China.

  2. Our lab Department of Transportation Engineering School of Mechanical & Vehicular Engineering  Four research teams Traffic Plan and Design Traffic Information and Management Logistic Operation Automobile Application Me B.S., Transportation Engineering, Beijing Institute of Technology, China. 2005 Paper: Public Transportation Query System of Beijing M.S., Transportation Engineering, Beijing Institute of Technology, China, 2007 Thesis: Research and Application of License Plate System Research Assistant in the Intelligent Transportation Lab Research Interests: Intelligent Transportation Systems, Image Processing and Pattern Recognition, GIS, Dynamic Traffic Assignment, Traffic Modeling and Simulation, Pavement Design. Basic Information

  3. Parking Guide System A project of our lab (demonstrated by the government for 2008 Olympic Games in Beijing) License Plate Recognition System Mainly introduce the plate localization process: correlation based method Brief Introduction Candidate Area Extraction Candidate Verification Experimental results Outline:

  4. Circumstance The parking lots of Cui Wei shopping mall in Beijing Two floors, 267 parking spaces Functions: Automatic parking guide Easily Management for employees Alleviate traffic pressure on the road Three Key Technologies Ultrasonic Detector CAN Bus Communication LED Billboard 1. Parking Guide System

  5. Data Collection Layer the basic layer collect the data from all parking spaces District Layer gather the data from Parking Space Detector e.g. one District Collector connect with 60 detectors Floor Layer floor layer controls all data from the corresponding district collectors Central Layer all data input to the central collector information broadcasting Layer Components

  6. Floor Controller Framework of Our Parking Guide System

  7. Parking Space Detector District Controller Central Controller

  8. 2.1 Brief Introduction Three parts license plate localization candidate extraction candidate verification character segmentation character recognition Difficulties weather, illumination license plate: size, color shoot angle pollution and abrasion shelter problem 2. License Plate Recognition System High quality camera and image processing board (arithmetic embedded in a digital processing chip)

  9. An Example: plate localization character segmentation character normalization and recognition

  10. Preprocessing and Rank Filter Searching Reference Lines Get candidate areas 2.2 Candidate Area Extraction (a) (b) (c) (d) (e)

  11. 2.3 Candidate Verification • Auto-Correlation Based Algorithm: Auto-correlation algorithm; (a) calculate the auto-correlation property; (b) auto-correlation curve; Base on the characteristic that plate area has seven block areas. The auto-correlation curve has about thirteen peaks for the car plate. (a) (b)

  12. Projection Based Algorithm: Verify the car plate using projection algorithm Verify the headlight area using projection algorithm

  13. Framework of proposed candidate verification method. In our experiment, th1=7, th2=20, th3=5, th4=20.

  14. Step 1: Extract plate which has light characters in dark background. • Step 2: Extract plate which has dark characters in light background.

  15. Step 3: Extract blue-white plate which has polluted by the light at night.

  16. 2.4 Experimental results • Database • 720*280 JPEG color images from a park entrance • 1704 images from three days surveillance are tested • Results • [1] : 88.1% • Proposed: 97.5% [1] V. Shapiro, G. Gluhchev, D. Dimov, Towards a multinational car license plate recognition system, Machine Vision Application, Volume 17, Issue 3, July 2006. pp. 173 – 183

  17. Thanks for your attention! Q&A