License Plate Detection, Recognition
and Automated Storage
students' project

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Results


Test images

In the first project stage, a set of test images has been prepared by using OLYMPUS CAMEDIA C-2040ZOOM digital camera. The image database contains over 500 images of the rear views of various vehicles (cars, trucks, busses), taken under various lighting conditions (sunny, cloudy, rainy, twilight, night light). The image database is available as a .zip archive.


Preprocessing

Several preprocessing algorithms have been tested in early stage of the project, by using Khoros Pro 2001 environment. The preliminary results, algorithm details and instructions on using Khoros Pro 2001 have been presented in documentation (available only in croatian).


Enhancing the contrast in greyscale images

The test images have been converted to grayscale and a number of greyscale image enhancement algorithms has been implemented and tested in order to obtain better image segmentation - license plate detection and extraction.
In the first project stage the following contrast enhancement algorithms have been implemented: histogram equalization, local histogram equalization (CLHE - Constrained Local Histogram Equalization), frequency domain algorithms and algorithm JGACE (Just Noticable Difference Guided Adaptive Contrast Enhancement). The testing of algorithms indicate that the most efficient algorithm is TGHE (Thresholding Histogram Equalization) which runs in 0.018 sec. on 640 x 480 images. The most time-consuming algorithm was the three-channel filtering with non-linear contrast stretching (0.555 sec. on 640 x 480 images).
The results have been evaluated by using some subjective as well as some objective criteria. First evaluation was based on viewers' subjective judgment of license plate readibility. The objective evaluation was based on the success of the subsequent license plate detection algorithm. Based on the evaluation results, a strategy based on gray-level histogram has been developed. For dark images the system uses TGHE algorithm with threshold set to 10. If the gray-level distribution is classified as convenient, the system uses the original grayscale image. In other cases the unsharp masking with three-channel filtering is used.

A detailed description of the implemented algorithms as well as some generated results can be found in documentation (available only in croatian). Some of the results are presented as PowerPoint slides (only in croatian) (short version, extended version).
A demo version of the program can be downloaded (here). The implemented algorithms can be tested on various images and with various parameter settings. Short instalation notes are (here).


Active-vision based extraction of vehicle image

As a part of the third project stage a system for extraction of the vehicle image from the scene has been developed. The task of this system is the surveillance of some passage where vehicles have to stop for some kind of control (customs border traffic control, parking lots - security and access control, toll collection control etc.) and the extraction of the image of the stopped vehicle. It is advantageous to capture the image containig only the vehicle, with as high resolution as possible, in order to improve subsequent processing.
Several subtasks can be identified:

  • vehicle detection;
  • vehicle position estimation;
  • image capture.
Such a system could be implemented in various ways. One approach is by using a sensor (e.g. photoelectric or step-on sensor) for the vehicle detection and triggering a camera to take a picture of the vehicle. As a triggering sensor we can use a camera connected to a computer, and apply computer vision methods to detect the vehicle, estimate its position and take a picture of it. In this implementation we use the latter approach, as it enables better flexibility and accuracy. Another advantage is improving the subsequent analysis because it enables taking the picture only of the most interesting part of the scene - the vehicle alone.

The system performance is illustrated by an image sequence. The program code is not available on the web because it depends on specific equipment (pan/tilt gimbal for camera) installed at the Department of Electronics, Microelectronics, Computer and Intelligent Systems at the Faculty of Electrical Engineering and Computing in Zagreb. A detaliled description of the implemented system can be found in documentation (available only in croatian),


Recognition of alphanumeric characters in license plates

During the project phase IV, a system for alphanumeric character recognition for license plate reading has been implemented. The system is based on multi-layer perceptron, which takes individual characters cut out from the thresholded image of the license plate to be read.
The system incorporates syntax analysis module which checks the recognized characters according to the syntax rules used for license plates in various countries. This module is able to correct some misrecognized characters, especially some very similar and otherwise indistinguishable characters (e.g. "0" i "O" ili "1" i "I"). The system has been tested on our image database with correct recognition ratio of about 90%. The recognition ratio could be improved by using finer image resolution, particularly by directing the camera and zooming only the license plate area. A detailed description of the implemented system can be found in documentation (available only in croatian). Some of the results are presented as PowerPoint slides (only in croatian) (short version, extended version).
A demo version of the program can be downloaded (demo). Short instalation notes are (here).


The compound system: automated license plate location, character extraction and character recognition

A prototype system has been implemented by linking afore mentioned subsystems. The system takes a digitized image of a vehicle, locates the license plate, extracts the characters and recognizes them by using multilayer perceptron.
A demo version of the program can be downloaded (demo). Short instalation notes are (here).


Publications

  1. Vlasta Srebrić, "Postupci poboljšanja kontrasta sivih slika", Diplomski rad br. 1397 (mentor: prof. dr. sc. Slobodan Ribarić) Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu, Zagreb, rujan 2003., 87 stranica, 6 stranica dodatka (PDF, in croatian).
  2. Goran Adrinek, "Segmentacija slike na temelju pokreta", Diplomski rad br. 1392 (mentor: prof. dr. sc. Slobodan Ribarić) Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu, Zagreb, srpanj 2003., 123 stranice ( PDF, in croatian)
  3. Kristijan Kraupner, "Uporaba višeslojnog perceptrona za raspoznavanje brojčano-slovčanih znakova na registarskim tablicama", Diplomski rad br. 1396 (mentor: prof. dr. sc. Slobodan Ribarić) Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu, Zagreb, rujan 2003., 104 stranice, (PDF, in croatian)
  4. Josip Haluška, "Razvoj programskih sustava u okruženju Khoros Pro 2001", Diplomski rad 1402, (mentor: prof. dr. sc. Slobodan Ribarić) Fakultet elektrotehnike i računarstva, Sveučilište u Zagrebu, Zagreb, veljača 2003., 93 stranice, (PDF, in croatian)
  5. S. Ribarić, G. Adrinek, and S. Šegvić, "Real-Time Active Visual Tracking System", submitted to MELECON 2004, (PDF)



Information Technology Application Project -- Ministry of Science and Technology

Copyright(c) Department of Electronics, Microelectronics, Computer and Intelligent Systems
Faculty of Electrical Engineering and Computing
University of Zagreb

Zoran Kalafatić
Last change: 10. 11. 2003.