IOT based real time number plate recognition for vehicle tracking and map generation

International Research Journal of Engineering and Technology (IRJET)

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IOT based real time number plate recognition for vehicle tracking and map generation Dharmishtha R. Chaudhari1, Samkit Shah2, Deep Patel3 1Assistant Professor, Dept. Of Comp. Sci. & Engg., R. N. G. P. I. T., Bardoli, Surat, Gujarat, India 2R. N. G. P. I. T., Bardoli, Surat, Gujarat, India

3 R. N. G. P. I. T., Bardoli, Surat, Gujarat, India

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Abstract - IoT based Real-time Number plate recognition

Number plate extraction is done using Sobel filter, morphological operations, and contour detection. We are using connected components and vertical projection analysis for Character segmentation. Segmented characters are recognized by the Support Vector Machine (SVM)[7][8] method. Recognized text is then stored in SQL Server Database. We took the support of Here Map API for map generation and representation.

for vehicle tracking and map generation is an important Technique, used in the Intelligent Transportation System. It is an important area of research because of its innumerable applications. Industries are adopting the fourth revolution.

But our society is still not adapted to this revolution.so, we try to integrate the concept of industrial revolution into our traffic system and try to make it a smart intelligent system by adapting the concept of the Internet of Things, Computer Vision, Cloud Computing and Visualization. We are proposing a real-time application which will recognize the number plate to track the path of the vehicle. The intelligent system provides data of vehicle number with place and time which can be used in follow up, analysis and monitoring vehicle. It will represent the path of the vehicle on the map.

2. Literature Survey Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition: Muhammad Tahir Qadri, Muhammad Asif. [1] described the method and system for Automatic number plate recognition (ANPR). The system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for character recognition. The resulting data is then used to compare with the records on a database so as to come up with the specific information like the vehicle owner, place of registration, address, etc.

Key Words: Number plate recognition, character segmentation, character recognition, ALPR, Intelligent transport, IoT, vehicle tracking, map generation

1. Introduction Vehicles are increasing enormously as they are necessary to travel from one place to another place in little time. We see the number of vehicles around us in our daily life and everyone needs it but with population increase, vehicles increased in large quantities. But it created disturbances to human life such as huge traffic, large sound, crime cases such as stealing of vehicles, accidents, etc. and therefore management of vehicles is very necessary. As a result, lots of improvements are being implemented in the current transportation system.

Vehicle Tracking Using Number Plate Recognition System: D. Madhu Babu, K.Manvitha, M.S.Narendra, A.Swathi, K.Praveen Varma [2] described the method to identify the lost vehicle and the vehicles which violate traffic rules. It is not possible to track the vehicle, by the user because they may not be able to identify the number of the moving vehicle. Therefore, it is necessary to capture the number plate of the vehicle and use this captured number to track the path of the vehicle. To track the path, they extract the number from the captured image, using JAVA OCRlibraries. Number plate identification is helpful in finding stolen vehicles, identification of the vehicle which violates traffic rules.

We are proposing a system which will maintain each and every log of a vehicle with time and place. It will use the Number plate as a unique entity to identify the vehicle details and use CCTV cameras to identify the location of the vehicle. This will work with existing CCTV cameras and attached with our system named IoT based Read time Number plate recognition for vehicle tracking and map generation. We can implement this system to the various sectors of society like stolen vehicle tracking, Military camps, Government offices, Automatic Parking system, Check post, Automatic toll plaza, etc.We develop number plate detection, recognition, and character segmentation.

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Automatic Number Plate Recognition and IoT Based Vehicle Tracking: Avadhut S Joshi, Digambar A Kulkarni [3] described the method and system for Automatic Number Plate Recognition and IoT Based Vehicle Tracking.

In this system, we recognize the vehicle number plate through the cameras, CCTV’s which will be converted from RGB image to Optical Character Recognition (OCR). This

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system also provides the feature for tracking the vehicle using its GPS location easily. The location of the vehicle can be traced and updated every ten seconds to track the vehicle continuously using Map along with data storage.

Here, every vehicle needs to carry the GPS.

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We process that frame as an image and extract a number plate from it. Then character segmentation and recognition is done on that extracted frame. Finally, we get the text of that vehicle number plate. We store that detail of the vehicle with date and location in the database. When a user searches for the vehicle then our system will generate a path and represent the traced path on the map [11] [12] [13].

Performance Analysis of Vehicle Number Plate Recognition System Using Template Matching Techniques: Gajendra Sharma. [5] Described the method of digital image processing techniques which is broadly used in vehicle transportation systems to identify the vehicle by their number plate. To identify vehicles by extracting the number plate and reading the plate to identify which unique identification code given to each vehicle. the performance of this method by comparing the result of the accuracy of the system using template matching algorithm normalized cross correlation and phase correlation algorithm.

4. Working of modules 4.1 Image Acquisition and pre-processing The high-resolution digital camera is used to acquire an image in this system. Images of the vehicle may be in different backgrounds, illumination conditions, and at various distances from. Images are resized to (1024 X 768). All the processing steps are executed on a grayscale image. The pre-processing is used to enhance the processing speed, improve the contrast of the image, and to reduce the noise in the image. It will remove the problem of low quality and low contrast in car images, images are enhanced by using histogram equalization on the grayscale image. The steps are the following:

3. System Flowchart This Flow Chart shows the overall flow of our system. It is a real-time system, so it analyses the live stream of a video camera. It converts video to frame and then finds the optimal frame.

1. Load RGB image and initialize variables: In this step, we read Red, green, and blue combined image as shown in figure

Fig -2 : Load RGB image and initialize variables 2. Convert Color to Grayscale: In this step, we convert color image to grayscale image as shown in figure 3.

Fig -3 : Convert Color to Grayscale Fig -1 : Flowchart of system

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3. Thresholding of frame: The image will be converted to black and white image in this Thresholding process. It will reduce the number of a range of the color scale from (0255) to (0-1)[11][13]. Finally, the subtracted grayscale image is converted into a binary image. Firstly the global threshold level is calculated by using Otsu's method[6] and after this according to the calculated threshold value; the subtracted grayscale image is converted into a black and white image. Figure 4 shows a binarized image.

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Fig - 6 : Vehicle number plate extraction After the detection of candidate number plate area, Bounding Box analysis is used to extract plate area from the original image. From the Bounding Box analysis, respective row and column indices of plate area are found out. Once the indices of number plate are known, the number plate is extracted from the original grayscale image.

Fig -4 : Apply threshold to the frame 4. Morphed Frame: Dilation operation is applied on this image for detection of candidate plate area. After this the unwanted portion of image is removed by using opening operation and finally the candidate plate region is detected by using morphological erosion operation. Then morphological opening and morphological erode operations are used for detection of exact candidate plate area (NPD) and its result is shown in Figure 5.

Fig - 7 : Bounding box on number plate 4.3 Character Segmentation & Recognition 4.3.1 Character Segmentation (CS): This step acts as a bridge between the number plate extraction and character recognition phase. In this phase, the characters on the number plate area are separated or segmented. There are many factors such as image noise, space mark, plate frame, plate rotation, and illumination variance etc. that make the character segmentation difficult. In the proposed approach the character segmentation is done by Connected Component Analysis (CCA) and Boundary Box Analysis (BBA)[9]. Firstly labels are assigned to connected components and the labeled characters are extracted using boundary box analysis as shown in figure 8 and 9.

Fig -5 : Mortphed frame 4.2 Number Plate area detection and Extraction: Fig - 8 : Character segmentation

After the detection of the number plate area that area is extracted from the image. The efficiency of number plate extraction depends on accurate detection of number plate area as shown in figure 6.

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Fig - 9 : Segmented Character

Fig - 11 : Generated Map

4.3.2 Character recognition (CR): It is the last phase of our system. The inputs to this phase are segmented characters and output of this phase is license plate number. The character recognition is done by template matching (TM) using correlation. Correlation is the degree of similarity between the segmented characters and the template characters. In the character recognition step firstly make the template by taking 42 X 24 pixel A to Z alphabet and 0 to 9 number images. Read all image and store them in the database and this result into 36 character templates. After the loading of templates, character normalization is done.

In character normalization, all the segmented characters are resized to template size 42 X 24.

5. Accuracy of results The results of various operations like plate localization, character separation and character recognition are shown with their success ratio in table 1 with 92%, 95.7 and 94.3% accuracy respectively.

Table 1: Success ratio with various operations Operation

Fail

Success Ration

100

92

8

92%

Character Separation

92

88

4

95.7%

Character Recognition

88

83

5

94.3%

plate

This project has many possible future enhancements to make the current intelligent traffic system better in terms of performance, features, and robustness. For instance, we can use it for measuring traffic density at a particular junction and it’s also possible to classify vehicle types. In addition, the system can be able to recognize number plate manipulation between two junctions and also detect illegal number plates. Furthermore, it can detect the validity of a vehicle from government rc book data and if it is going to expire then notify the user about it and if in the given time, the user does not take any action towards it then important steps would be taken by police.

7. CONCLUSIONS In our present work, we have developed a complete system for recognizing the license number of a vehicle.

The image processing technique successfully identifies the number plate of the vehicle for around 93% cases. The accuracy of the system is measured in terms of its constituent modules and is summarized in Table 1.

Though the proposed system performs well in real life outdoor scenarios in India, still there are some limitations

4.3.3 Map Generation When the user searches for a vehicle in our system, we fetch data of the vehicle from the database and generate the traced path. The generated path will be displayed on the Here Map as shown in figure 11. Here we use Map API for Indian Map support.

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Success

6. Future Enhancements

Fig - 10 : Reorganization of Character using OCR

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Sample

License localization

Sometimes the segmented characters do not have the same size so the better way to overcome this problem is to resize the characters into one size (equal to template size) before actual recognition starts. In last the segmented characters are matched with template characters using correlation. The similarity between the template characters and segmented characters is measured and the template that is most similar to the character is recognized as a target. The value of correlation is calculated by comparing the normalized segmented character image with each template character image and selecting the most relevant image and writes that character as shown in figure 10.

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of the current work. First of all, we have designed a system to work for most vehicles in the city. The system may be developed for real-time tracking of high-speed vehicles.

The system fails if the license plate is partially or totally occluded by others.

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8. Avadhut S Joshi, Digambar A Kulkarni on “Automatic Number Plate Recognition and IoT Based Vehicle Tracking” https://www.irjet.net/archives/V4/i7/IRJETV4I7319.pdf. On July -2017 9. Shan Du,Mahmoud Ibrahim,Mohamed Shehata and Wael Badawy, “Automatic License Plate Recognition (ALPR):A State-of-the-Art Review”, IEEE Transactions on Circuits & Systems for Video Technology, Vol. 23, Issue.2, pp.311325, 2013.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

The system developed for stolen vehicles can easily be modified to apply it in other license plate recognition systems, like a recording of vehicles ‘entry in toll plaza, in car parking areas etc. Keeping the domain of operation and the outdoor run time environment in mind and considering the simplicity of the system compared to the complexity of the problem the overall performance of the system may be considered to be satisfactory.

10. Christos-Nikolaos E. Anagnostopoulos, “License Plate Recognition: A Brief Tutorial”, Intelligent Transportation Systems Magazine (IEEE), Vol.6, Issue.1, pp.59-67, 2014 K. Elissa, “Title of paper if known,” unpublished.

REFERENCES 1. Muhammad Tahir Qadri and Muhammad Asif, “Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition”, in 2009 International Conference on Education Technology and Computer, Singapore.

11.C. Chunyu, W. Fucheng, C. Baozhi and Z. Chen,” Application of image processing to the vehicle license plate recognition,” International Conference on Computer Science and Electronics Engineering, published by Atlantis Press, pp 2867-2869, 2013 Y.

2. D.Madhu Babu, K.Manvitha, M.S. Narendra, A.Swathi and K.Praveen Varma,"Vehicle Tracking Using Number Plate Recognition System”, in International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 6 (2), 2015, 1473-1476, Vizianagaram, India.

12. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf.

Magnetics Japan, p. 301, 1982].

3. Avadhut S Joshi and Digambar A Kulkarni,"Automatic Number Plate Recognition and IoT Based Vehicle Tracking”, in International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 07 | July -2017, Karnataka, India.

13. G. Naveen Balaji, V. Narayanan,V.S. Nivash “Low Power and High performance JK Flip – Flop using 45 nm Technology” International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE) Vol 3, Issue 10, October 2016, pp:26-29

4. Divya gilly and Dr. Kumudha Raimond, “License Plate Recognition- A Template Matching Method”, International Journal of Engineering Research and Applications (IJERA), Vol. 3, Issue. 2, pp.1240-1245, 2013.

14.G.C.Lekhana and R.Srikantaswamy,” Real time license plate recognition system,” International Journal of Advanced Technology & Engineering Research, Vol-2, Issue-4, pp 5-9, July 2012.S Kranti and K Pranathi,” Automatic number plate recognition,” International Journal of Advancements in Technology,vol-2, no-3, pp408-423, July 2011.

5. Iroegbu Chibuisi and Okoronkwo Madubuezi C,"MATLAB Based Vehicle Number Plate Recognition”, in International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 3 Issue IV, April 2015, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria.

6. MuhammadTahir Qadri,Muhammad Asif .ISBN- 21551812 https://ieeexplore.ieee.org/document/5169511 IEEE on 21 July 2009 7. D.Madhu Babu , K.Manvitha, M.S.Narendra , A.Swathi ,K.Praveen Varma on “Vehicle Tracking Using Number Plate Recognition System” ISSN-0975-9646 http://ijcsit.com/docs/Volume%206/vol6issue02/ijcsit2 0150602123.pdf.-July 2015

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