An intelligent strategy for checking the annual inspection status of motorcycles based on license plate recognition Yo-Ping Huang a, Chien-Hung Chen b, Yueh-Tsun Chang b, Frode Eika Sandnes Expert Systems with Applications 36 (2009) 9260–9267 reporter ： Shih,wen-feng Lee,zhi-you

Outline  Introduction  System overview  Method  Experimental and Results  Conclusion 2

Introduction(1/5)  Motorcycles are one of the most commonly used forms of transportation in Southeast Asia in general, and in Taiwan in particular, due to their low cost, the high concentration of people and the traffic congestion.  The increase in motorcycles on the road results in new problems such as more thefts and more pollution. 3

Introduction(2/5)  Currently, the police have set up temporary monitoring stations along the roadside to conduct inspections.  However, when approaching these stations most motorcyclists will accelerate to escape the inspection. Consequently, less than 50% of all motorcycles have been inspected. 4

Introduction(3/5)  A license plate recognition system is proposed to help improve the efficiency and convenience of checking motorcycle status at roadside and designated inspection points.  A complete license plate recognition system consists of three major parts: license plate location, license plate segmentation and character recognition. 5

Introduction(4/5)  Several strategies have been proposed for successfully locating license plates in images.  In particular, fuzzy c-mean clustering combined with a filtering strategy is especially suitable for locating license plates.  In this study a computationally more effective approach is taken based on the integrated horizontal and vertical projections with a search window. 6

Introduction(5/5)  In the past, optical character recognition (OCR) techniques were used to recognize license plate characters.  In this study, a hybrid method based on back propagation neural networks and feature matching is proposed whereupon the respective strengths of both methods are combined. 7

System overview(1/2) Table 1. Motorcycle typeLicense plate colorCharacter colorExample of license plate LightGreenWhiteAXX-NNN NNN-AAA Regular heavyWhiteBlackAXX-NNN NNN-AAA Heavy (250–550 c.c.)YellowBlackNNN-AAA Heavy (550 c.c. ∼ ) RedWhiteAA-NN 8

System overview(2/2) 9

Method(Locating license plates(1/5)) 10

Method(Locating license plates(2/5))  Since some images are taken in outdoor environments the image quality may be affected by the luminance, reflections and shadows.  Before applying edge detection, a 3 × 3 median filter is used to reduce the noise in the image.  since the license plate is located at the lower part of an image the projection histograms are scanned from the bottom to the top such that the height of the license plate can be quickly identified. 11

Method(Locating license plates(3/5)) 12

Method(Locating license plates(4/5))  Normally, it is easy to locate the license plate by vertical projection. However, there are situations where the license plate cannot be easily located through vertical projections alone.  the width to height ratio of a license plate is about 3 to 1. 13

Method(Locating license plates(5/5))  image may contain irrelevant areas that may be misrecognized as part of the license plate. Therefore, a simple method is used to remove these. 14

Method(License plate segmentation(1/2)) 15

Method(License plate segmentation(2/2))  If the license plate cannot be correctly located the character recognition is likely to consequently fail.  When Lh is greater than Rh by a threshold TL, the characters on the right side are recovered. Similarly, if Rh is greater than Lh by another threshold TR, the characters on the left side are recovered. 16

Method(Character recognition(1/5)) 17

Method(Character recognition(2/5))  The 26 vertical and 50 horizontal projections of the normalized 26 × 50 pixel license plate image are fed into the 76 input nodes of the back propagation neural network. 18

Method(Character recognition(3/5)) 19

Method(Character recognition(4/5))  Most license plate characters are successfully recognized by the back propagation neural network. However, characters such as B and 8, 1 and I, and O and D may be hard to distinguish using the neural network.  A straight line is posted to the character as the base line to respectively accumulate the number of white pixels at the upper and lower left corners. 20

Method(Character recognition(5/5)) 21

Experimental and Results 22

Experimental and Results Table 2. Performance evaluation of the proposed system. Inspection pointsRoadside Images PlatformPCUMPC Location rate96.70%98.40% Recognition rate97.10%97.30% Overall performance93.90%95.70% Average time0.293 s0.654 s 23

Experimental and Results 24

Conclusion  Recognition is performed in three phases, namely locating the license plate area in an image, the segmentation of characters and the recognition of characters.  The horizontal and vertical projections are scanned using a search window to locate the license plate. A character recovery strategy is employed to enhance the locating rate.  An instant recognition system will be able to provide autonomous real- time motorcycle surveillance and reduce human efforts. 25

Thanks for your attention! 26