1. © 2008 Chen Hao, Beijing Institute of Technology 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. chenhaoits@gmail.com http://chenhaoits.cn

2. © 2008 Chen Hao, Beijing Institute of Technology 2 Basic Information  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.

3. © 2008 Chen Hao, Beijing Institute of Technology 3 Outline ：  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

4. © 2008 Chen Hao, Beijing Institute of Technology 4 1. Parking Guide System 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

5. © 2008 Chen Hao, Beijing Institute of Technology 5 Layer Components 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

6. © 2008 Chen Hao, Beijing Institute of Technology 6 Framework of Our Parking Guide System Floor Controller

7. © 2008 Chen Hao, Beijing Institute of Technology 7

8. 8 Parking Space Detector Central Controller District Controller

9. © 2008 Chen Hao, Beijing Institute of Technology 9 2. License Plate Recognition System 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 High quality camera and image processing board (arithmetic embedded in a digital processing chip)

10. © 2008 Chen Hao, Beijing Institute of Technology 10 plate localization character normalization and recognition character segmentation An Example:

11. © 2008 Chen Hao, Beijing Institute of Technology 11 2.2 Candidate Area Extraction Preprocessing and Rank Filter Searching Reference Lines Get candidate areas (a) (d)(e) (b)(c)

12. © 2008 Chen Hao, Beijing Institute of Technology 12 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. 2.3 Candidate Verification (a) (b)

13. © 2008 Chen Hao, Beijing Institute of Technology 13 Projection Based Algorithm: Verify the car plate using projection algorithm Verify the headlight area using projection algorithm

14. © 2008 Chen Hao, Beijing Institute of Technology 14 Framework of proposed candidate verification method. In our experiment, th1=7, th2=20, th3=5, th4=20.

15. © 2008 Chen Hao, Beijing Institute of Technology 15 Step 1: Extract plate which has light characters in dark background. Step 2: Extract plate which has dark characters in light background.

16. © 2008 Chen Hao, Beijing Institute of Technology 16 Step 3: Extract blue-white plate which has polluted by the light at night.

17. © 2008 Chen Hao, Beijing Institute of Technology 17 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% AlgorithmsImagesCorrectNo Plate [1]17041501188 Proposed1704166121 [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

18. © 2008 Chen Hao, Beijing Institute of Technology 18 Thanks for your attention! Q&A