Abstract
Automatic number plate recognition (ANPR) is the method of extraction of vehicle number plate information from an image or a sequence of images. The extracted data can be used in many applications, such as toll booths and vehicle parking areas where payment can be done electronically and traffic surveillance systems. The images are taken by the ANPR systems using either a color , black and white or an infrared camera, depending on which different techniques are applied for extraction of information. The quality of image plays a vital role in the successful extraction of license data. For ANPR to be a real life application it has to quickly and successfully process license plates under different environmental conditions, such as day
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or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images.
The license plates can be partially occluded by dirt,
lighting, and towing accessories on the car. In this paper, we
present a comprehensive review of the state-of-the-art techniques
for ALPR. We categorize different ALPR techniques according
to the features they used for each stage, and compare them in
terms of pros, cons, recognition accuracy, and processing speed.
Future forecasts of ALPR are given at the end.
I. Introduction
AUTOMATIC license plate recognition (ALPR) plays an important role in numerous real-life applications, such
as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring [1]–[4].
ALPR recognizes a vehicle’s license plate number from an
image or images taken by either a color, black and white,
or infrared camera. It is fulfilled by the combination of a
lot of techniques, such as object detection, image processing,
and pattern recognition. ALPR is also known as automatic
vehicle identification, car plate recognition, automatic number
plate recognition, and optical character recognition (OCR) for
cars. The variations of the plate types or environments cause
challenges in the detection and recognition of license plates.
They are summarized as follows.
1) Plate
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variations: a) location: plates exist in different locations of an image; b) quantity: an image may contain no or many plates; c) size: plates may have different sizes due to the camera distance and the zoom factor Introduction Related work Methodology: Image Segmentation Module In this step,the contrast of image is enhanced and image is binarised using OTSU’s thresholding method. The binarised image is then complemented for connected component analysis. Character Detection and Filtering using Connected Component Analysis The segmented regions of the image are then screened to identify plausible characters. First, the module checks simply the size and area constraints hmin < h < hmax wmin < w < wmax area min < area where h,w and area are the height, width and area of the segment’s axes aligned bounding box respectively, and hmin , hmax , wmin ,wmax and areamin are parameters of the module. Each element that satisfies these constraints are then tested using character / non-character classifier based on shape descriptors. Character / Non-character classifier Character / Non-character classifier is an important part for the localisation step of the license plate. It is based on shape descriptors which uses HOG features for classification. The classifier is trained using the features extracted from the training set. These shape descriptors are fed to a multiclass SVM(Support Vector Machine). The output of the SVM gives Character / Non-character decision. The HOG-based Character / Non-character classifier described in the literature, generally divide the image into a 2D array of nx * ny cells and compute a separate histogram of gradient orientations with a fixed number nb of bins within each cell, as done by Dalal and Triggs for human body recognition [ ]. Training of SVM SVM was trained for two different sets of binary images. One for the characters and the other one for the non-character elements. The database consisted of 400 images of both the types. The whole database was used for the training purpose rather than splitting it into training and testing sets. For license plates detection purposes, only uppercase letters were taken in consideration for the character set.Thus reducing the effect of other text present in image which could have been misidentified as region having License plate. The character grouping algorithm further determines that only such Regions of Interest remain in the binary image that can contain License Plate. The output of the classifier was thus only the regions having uppercase letters and some non character elements which passed the classifier and was labelled as text. Character grouping module Character grouping module is created to join the characters recognised by classifier into text regions. All the classified characters are arranged according to their y coordinate. For a given (y+a) coordinate range if the number of characters is above 2, then only the coordinates are considered. The characters after the above step of horizontal elimination is considered for vertical separation or elimination.
Two characters are grouped together if the gap between the bounding boxes found along the x and y coordinate are less than (p1* h) and (p2*h) where h is height of the bounding box and p1 , p2 are system parameters. Here it is noteworthy that these gaps are the actual gaps present between the two individual characters in the number plates along x axis and y axis accordingly. The gap between the characters along x axis varies in a finite range while the gaps along the y axis are fixed for a dual line license plate as is seen in the image below. But for the case of tilted license plates the gap along y axis also changes in accordance to the the angle of
tilt. In case the gap between two boxes are zero or negative, as is possible in the case of broken letters, then the two bounding boxes are merged together. Figure : Different types of license plates After this, the median value of height of boxes is taken as a reference to select the elements that will pass the grouping module. Localisation of license plate : Skew correction: License plate validation:
Over the past few years, the parking control office has been expanded and made more efficient by the addition of computers and the implementation of new filing procedures. Unfortunately, the department is still not providing the services it is capable of. This is due to the fact that the parking control office is under staffed in respect to counter attendants. This lack of sufficient people to work the front counter causes the process of appealing and paying parking tickets to become extremely bogged down. This proposal provides a cost effective solution to the problem that will not only help parking control, but will also benefit anyone doing business with the office.
The use of ANPR Automatic Number Plate Recognition (ANPR) technology is used to help detect, deter and disrupt criminality at a local, force, regional and national level, including tackling travelling criminals. ANPR provides lines of enquiry and evidences in the investigations of crime and is used by forces throughout UK, Wales and Northern Ireland. How it works As a car or a vehicle passes an Automatic Number Plate Recognition (ANPR) camera, its registration number is read and checked thru database records of cars. Police officers can stop a car, check it for evidence and, where necessary, make arrests. The use of ANPR in this way has proved to be important in the detection of many offences, including locating stolen cars, tackling uninsured car use and uncovering cases of major crime.
Companies like Google, Tesla and Nissan, among others, have announced over the past few years that their companies are trying to develop self-driving or autonomous cars [Ref. 1 and 2]. Self-driving cars can provide many benefits to the average consumer. Studies have shown that because computers can react and process information many times faster than a human being, crashes on streets and roads can be decreased with quick and consistent evasion maneuvers by the autonomous car. They can also help maximize fuel economy by calculating the most direct and fastest routes. When the driving of an autonomous car demonstrates that the computer can safely and reliably transport the passengers to their destination, this frees up the passengers to do other things that they would not normally be able to do if they were driving the car manually. For this reason, self-driving cars can help maximize productivity of their passengers.
In this project, issues regarding the Hough Transform for line detection are considered. The first several sections deal with theory regarding the Hough transform, then the final section discusses an implementation of the Hough transform for line detection and gives resulted images. The program, images, and figures for this project are implemented using the Matlab.
Traffic Control System was then and is now operating on World War II era technology with most
A barcode is a visual representation of data that is checked and utilized for data. Bar code is simple to control equipment and tool inventory. In facilities supervisors can use barcodes to link work orders, purchase orders, spare parts and equipment which can be further used to track and collect cost. In barcodes historical data can be used to predict the seasonal fluctuation accurately. Barcodes are printed specifically on the paper or a plastic object, therefore actualizing a standardized identification framework is far less expensive than the RFID innovation.
Technology is evolving faster than ever these days, however there is one technology that could revolutionize the transportation industry. This technology is called autonomous cars, also known as self-driving cars. Autonomous cars can be defined as a vehicle that is capable of sensing its environment, and navigating without human input. Using different techniques such as GPS and radar, autonomous cars can detect surroundings, thus removing the human element in driving. This would have a positive effect in more ways than we could ever imagine. Research suggests that self-driving cars will become more abundant in the future because they will be more cost-effective, enhance safety, and decrease traffic congestion.
For each train length, we started by only having that car number. Then we would subtract one from the total train length and laid out whichever car number would help equal the train length. We kept going down that line of pattern subtracting one each time until we got to having all ones to equal the train length. Each time we made sure that we changed around the car numbers to make sure they were all laid out in every single way possible. For example, for train number 4, we started with laying out car number 4 and then starting with car number 3 after that.
JIAFILMS is an irregular license plate name. You might be pondering what is the meaning of this name? Obviously, it's my last name with “films” at the end. Other than that, this tale begins, 4 years ago, with three girls. Their names were Kaylee Yu, Jennifer Jia, and Jacquelin Jia. These girls were inseparable, although they lived 30 minutes apart. One day, they were playing with Kitty; Kaylee’s dog (yes the dog’s name is Kitty) outside in a garden, they were bored like always, then suddenly, Kaylee had a suggestion. She suggested, “we could pass the time by filming a random movie?” It was a peculiar and insane idea. Nonetheless, it was harmless and fun and they were only doing it to have time fly by right?
The Ultimate Vehicle Security System. (2001). Retrieved September 18, 2001 from the World Wide Wed: http://www.powerlock.com/plfront.htm
with a digital map, who shows the position of the car. Based on the position of
So we are going to design a system which aims to improve parking facilities by the introduction of a smart car parking system. The system will automatically assign a vacant space to the patrons for parking their vehicles. The patron will be guided to the specified location by referring to variable message sign and the map and the location printed on the parking ticket, in which the whole process is monitored by a central computer to store and update the occupancy status of available parking space vacancies in the database.
Furthermore, in relation to the regulations and rules relating to automated vehicles, the federal agencies with the responsibility for automated-related factors on the nation’s highways include the National Highway Traffic Safety Administration (NHTSA), the Federal Highway Administration, and the Federal Motor Carrier Safety Administration (FMCSA). NHTSA has the responsibility of reducing deaths, injuries, and financial losses cause to vehicle accidents. FMCSA serves a similar purpose for large trucks and buses. However, because there are no specific federal policies or regulations in place that govern the use, operation, or deployment of automated technology, NHTSA has stated that many vehicle technologies are deployed without regulation being in place that allows their use. However, NHTSA makes it clear that technologies cannot pose an “unreasonable risk to safety.” Because of this, many technologies see significant market penetration before standards are developed. One factor leading to this situation is that there is typically a five- to eight-year timeframe for regulation development and activation.
Barcodes are used everywhere around us. They are used to track products through shipment, track products at a store and speed up and enhance the checkout process, as well as allowing faster access to information. Barcodes began to be used heavily in the 1970’s. This began a great movement in the consumer industry, speeding up the checkout process and allowing easier inventory tracking. However, just like all technologies, barcodes have been enhanced many times over and are being replaced by better, more efficient systems (Bonsor).
Traffic engineers and planners need information about traffic. They need information to design and manage road and traffic system. They use the information for planning and designing traffic facilities, selecting geometric standards, economic analysis and determination of priorities. They use this to justify warrant of traffic control devices such as signs, traffic signals, pavement markings, school and pedestrian crossings. The also use this information to study the effectiveness of introduced schemes, diagnosing given situations and finding appropriate solutions, forecasting the effects of projected strategies, calibrating and validating traffic models.