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Automatic Number Plate Recognition

ECE Technical seminar

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Automatic Number Plate Recognition

  1. 1. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 1 CHAPTER 1 INTRODUCTION Law enforcement agencies throughout the nation are increasingly adopting automated license plate recognition (ALPR) technologies to enhance their enforcement and investigative capabilities, expand their collection of relevant data, and expedite the tedious and time consuming process of manually comparing vehicle license plates with lists of stolen, wanted, and other vehicles of interest. Police officers, sheriff’s deputies, and other law enforcement practitioners are often on the lookout for vehicles that have been reported stolen, are wanted in connection with a crime or traffic violation, are suspected of being involved in criminal or terrorist activities, are parking violation scofflaws, have failed to maintain current registration or to comply with statutory insurance requirements, or any of a number of other legitimate reasons. ALPR systems function to automatically capture an image of the vehicle’s license plate, transform that image into alphanumeric characters using optical character recognition or similar software, compare the plate number acquired to one or more databases of vehicles of interest to law enforcement and other agencies, and to alert the officer when a vehicle of interest has been observed. The automated capture, analysis, and comparison of vehicle license plates typically occur within seconds, alerting the officer almost immediately when a wanted plate is observed. Although the ALPR term includes a specific reference to “automated,” it should be noted that human intervention is needed insofar as the officer monitoring the equipment must independently validate that the ALPR system has accurately “read” the license plate, that the plate observed is issued from the same state as the one in which it is wanted, and to verify the currency of the alert, i.e., verifying that the reason this vehicle or the owner was wanted or of interest is still valid. This National Institute of Justice (NIJ)–supported project was designed to assess and document ALPR implementation and operational experiences among law enforcement agencies in the United States, and to identify emerging implementation practices to provide operational and policy guidance to the field. Several data collection techniques were used to gather information for this project, including: 1) A survey of law enforcement agencies to assess the scope of the current ALPR implementation, deployment, and operational uses, 2) Site visits to interview law enforcement practitioners and observe ALPRs system in operation, 3) Reviewing documents and policies addressing ALPR implementation and use.
  2. 2. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 2 This report includes sample ALPR policies from several jurisdictions to assist readers in developing their own policies. Readers are also encouraged to review a supplemental report, “Privacy issues concerning the utilization of automated license plate readers,” previously prepared by IACP as part of an effort to develop a privacy impact assessment, in developing ALPR policies for their agencies. 1.1 Background Owners of motorized vehicles driven on public thoroughfares are required by law to annually register their vehicles with their state bureau or department of motor vehicles, and to attach license plates that are publicly and legibly displayed. Vehicle license plates generally consist of a series of alpha numeric characters that reference the license plate to the specific vehicle registered (including the make, model, year, and vehicle identification number (VIN)) and the owner and/or lien holder of the vehicle. New York is credited as the first state to enact legislation requiring vehicle registration on April 25, 1901, with California following in 1902. In Delaware, where numbered license plates were first issued in porcelain in 1909 beginning with a numbering sequence of 1000, the state changed the numbering scheme in 1910, beginning with the number “1”, which is reserved for the Governor. Delaware license plates are sold to the owner of the vehicle and can be passed down generation to generation. In 2008, a man and his son paid $675,000 in private auction for license plate number “6”and this figure was matched for Delaware license plate number “11” the following year. Contemporary license plates, which measure 6 x 12 inches in the United States, feature numbering schemes that vary from state to state. States typically use numbers or a combination of letters and numbers in their vehicle license plates. Some states, like Maryland, use stacked letters—one over the other. Figure 1.1: Examples of Different State License Plate Numbering Schemes Connecticut is credited with being the first state to issue vanity plates beginning in 1937, when “motorists with good driving records were allowed to have plates with their initials (2 or 3 letters).” In Texas any person, non-profit organization, or for-profit entity can design a specialty license plate for consideration and potential adoption by the state for an initial deposit of $4,615, which will be refunded to non-profit organizations after 500 of the plates are sold or renewed. A Texas plate with “PORSCHE” recently sold in private auction for $7,500, “AMERICA” for $3,000, and “FERRARI” for $15,000.
  3. 3. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 3 Fig:1.2:Examples of Specialty License Plates for Passenger Vehicles Specialty plates generate substantial revenue for states. West Virginia, for example, reports approximately $1.2 million in revenue from the sale of vanity license plates. Texas is estimated to have generated approximately $2.1 million in revenue from the sale of specialty plates through the first 10 months of 2010, Virginia projected potential revenue exceeding $1 million for the sale of specialty plates with company logos, and approximately $600,000 of revenue generated in Nebraska in 2009 from sales of its “Huskers” license plates. The California Legislature recently considered a bill to study the potential use of electronic license plates which would show digital advertisements when the vehicle to which it is attached is stopped for more than four seconds; the vehicle’s license plate number would display when the vehicle is in motion. 1.1.1Police use of license plate data As noted above, law enforcement practitioners are often searching for vehicles that have been reported stolen, are suspected of being involved in criminal or terrorist activities, are owned by persons who are wanted by authorities, have failed to pay parking violations or maintain current vehicle license registration or insurance, or any of a number of other legitimate reasons. Victims and witnesses are frequently able to provide police with a description of a suspect’s vehicle, including in some cases a full or partial reading of their license plate number. Depending on the seriousness of the incident, officers may receive a list of vehicles of interest by their agency at the beginning of their shift, or receive radio alerts throughout the day, providing vehicle descriptions and plate numbers including stolen vehicles, vehicles registered or associated with wanted individuals or persons of interest, vehicles attached to an AMBER or missing persons alert, and “be on the lookout” or “BOLO” alerts. These lists may be sizable depending on the jurisdiction, population size, and criteria for the list, and can present challenges for the patrol officer.
  4. 4. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 4 Officers monitor traffic during patrol, searching for vehicles of interest among their other duties. When a potential vehicle of interest is observed, the officer will typically compare characteristics of the observed vehicle and driver with those of the wanted vehicle, including the license plate number, if known. If warranted, the officer may stop the vehicle to further investigate. A license plate check will be run on the vehicle, either by the officer using an in-field computer to initiate the query, or by radioing dispatch for the query. Results of the query and of the officer’s interaction and investigation of the driver will assist the officer in determining next steps. In addition to spotting vehicles of interest, officers on patrol are also alert to vehicles with expired or missing license plates and annual renewal tags. Failure to maintain current license plate registration may indicate that one or more of several conditions have not been met, including failure to secure vehicle insurance, failing compulsory safety and/or emissions inspections, and simple failure to properly register the vehicle with the state motor vehicle authority. Some jurisdictions may withhold vehicle registration renewal if the owner has unpaid parking or traffic tickets. The lack of a plate or current tags may also indicate that the vehicle has been stolen. 1.1.2 Automated Number Plate Recognition (ANPR) Technology Automated Number plate recognition (ANPR) technology was invented in 1976 in the Police Scientific Development Branch (PSDB), Home Office, United Kingdom. The European Secure Vehicle Alliance (ESVA) notes that the “Provisional Irish Republican Army (IRA) terrorist bombings in the City of London resulted in the establishment of the ‘ring of steel’ in 1993 – a surveillance and security cordon using initially CCTV cameras. In 1997, ANPR cameras, linked to police databases, were fitted at entrances to the ring of steel and gave feedback to monitoring officers within four seconds.” Implementation continued over the next several years with forces implementing ANPR systems. The Home Office Police Standards Unit and the Association of Chief Polices Officers (ACPO) began testing dedicated “intercept teams” using ANPR across nine police forces in the multi phased “Project Laser” beginning 2002.The strategic intent of the ANPR strategy for the Police Services was to “target criminals through their use of the roads.” Intercept teams, typically ranged in size of 7 – 12 officers and equipped with ANPR, were designed to enable police to engage criminality on the road and intercept vehicles and drivers wanted in connection with crime, terrorism, and motor vehicle violations. An analysis of the Laser pilot
  5. 5. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 5 projects, which collectively produced over 46,000 arrests, concluded that “ANPR makes a direct contribution to both national and force objectives and is used on a daily basis to engage criminals. In comparison to a number of other technology-enabled projects in the criminal justice area, its success has been remarkable.” Following success of the Laser pilots, the Police Standards Unit invested £32 million for development of the National ANPR Data Centre (NADC) and a Back Office Facility (BOF), which provide data storage and analytic tools for forces in England and Wales, and support deployment of ANPR at national, regional and local levels. Implementing a single technology platform in forces across the whole of England and Wales has enabled the UK to implement universal business practices and technical and data standards. By the end of the first quarter of 2010, the NADC was receiving approximately 10-12 million license plate reads per day from over 5,000 ANPR cameras, had the capacity to receive up to 50 million reads per day, and maintained a database of more than 7 billion vehicle sightings. ALPR also has many applications beyond law enforcement. It is used by departments of transportation to monitor travel time on key roadways for better traffic management (where ALPR captures images of vehicles at two different points on a roadway and calculates travel times between the two points), automated tolling and toll enforcement, access control, and congestion charging, among other things.
  6. 6. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 6 CHAPTER 2 AN OVERVIEW OF ANPR ANPR systems generally consist of a high speed camera with an infrared (“IR”) filter or two cameras—one high resolution digital camera and one IR camera—to capture images of license plates; a processor and application capable of performing sophisticated optical character recognition (OCR) to transform the image of the plate into alphanumeric characters; application software to compare the transformed license plate characters to databases of license plates of interest to law enforcement; and a user interface to display the images captured, the results of the OCR transformation, and an alert capability to notify operators when a plate matching an agency’s “hot list” is observed. The precise configuration of ALPR systems varies depending on the manufacturer of the equipment and the specific operational deployment. ALPR systems are able to capture up to 1,800 plates per minute at speeds up to 120- 160 miles per hour. Systems range in cost from $10,000 - $22,000, depending on the manufacturer and the specific configuration specified, and agencies have often been able to fund acquisition through federal grant funding sources. 2.1Cameras: Camera hardware is significant to the front-end component of any ALPR system. Since the initial image capture forms a critically important part of the ALPR system and will often determine the overall performance, ALPR systems typically use still or video cameras Fig 2.1: Cameras Specialized for the task. Currently, many of the ALPR systems include a set of high resolution digital and IR illuminated cameras which allow the ALPR system to capture images under a variety of light and weather conditions.
  7. 7. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 7 2.2 User Interface In vehicle-mounted ALPR systems, captured images are displayed on a user interface either a dedicated computer for the ALPR system, or use of the in-field computer already installed in the police vehicle so, the officer can be alerted when a vehicle on one of the hot lists has been observed in the vicinity of the officer. The user interface allows the officer to compare the ALPR OCR interpretation of the license plate number to ensure the accuracy of the read and to see the larger, contextual image to help the officer in identifying which specific vehicle has the plate of interest. In addition, the user interface also typically enables the officer to manually enter plates on vehicles of interest, manage hot list information, deal with alert queues, and run reports. 2.3 Software: Fig 2.2: operation through computer As vehicles pass through the field of view of the ALPR camera a picture is taken of license plate and the vehicle. A series of algorithms are performed on the image to isolate the plate and render the alphanumeric characters into an electronically readable format. The sophistication and complexity of each of these algorithms determines the accuracy of the system. There are six primary algorithms that the software requires for identifying a license plate: 1. Plate localization – Finding and isolating the plate on the picture. 2. Plate orientation and sizing – Compensates for the skew of the plate and adjusts the dimensions to the required size.
  8. 8. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 8 3. Normalization – Adjusts the brightness and contrast of the image. 4. Character segmentation – Finds the individual characters on the plates. 5. Optical character recognition (OCR) – Translation of images of text into an electronically readable format. 6. Syntactical/Geometrical analysis – Check characters and positions against state-specific rules to identify the state of issuance for the license plate. 2.4 Hot lists: Once the OCR read is obtained, the information is then compared against a database of vehicles of interest, typically known as a “hot list.” Hot list information can come from a variety of sources, and is discussed in more detail later in this report. The purpose of these lists is to alert the officer that a vehicle displaying a license plate number that is included on a hot list has been identified by the ALPR camera. ALPR systems can be deployed in a variety of ways, including mobile ALPR systems, fixed ALPR systems, and portable ALPR systems.
  9. 9. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 9 CHAPTER 3 PROCESS OF AUTOMATIC NUMBER PLATE RECOGNITION The process of Automatic Number Plate Recognition consists of four main stages: 1. Pre-processing 2. License plate localization 3. Character segmentation 4. Character recognition 3.1 Pre-processing: As mentioned before, the system of automatic number plate recognition faces many challenges. So, this step is essential to enhance the input image and making it more suitable for the next processing steps. The first step done in the pre-processing is to apply minimum filter to the image in order to enhance the dark values in the image by increasing their area. This is mainly done to make the characters and the plate edges bold, and to remove the effect of the light diagonal strips that appear in the characters and edges of the Egyptian license plates. This process is followed by increasing saturation of the image to increase the separation between colours. Then the image is converted to gray scale (taking the luminance component of NTSC). Then, increasing the image contrast to separate the background from highlights. 3.2 License Plate Localization: In this stage, the location of the license plate is identified and the output of this stage will be a sub-image that contains only the license plate. This is done in two main steps. 3.2.1 Locating a large bounding rectangle over the license plate In this step a rectangle that contains the license plate is located (this rectangle may also has some extra parts from the four sides), and this rectangle is the input to the next step for further processing (removing the extra parts, character segmentation then recognition).
  10. 10. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 10 Figure 3.1:Large bounding rectangle over the license plate-1 First, Sobel vertical edge detection is applied to the image. Then a threshold of 36 (This value is determined using trial and error) is applied, such that every edge with magnitude less than 36 is considered false edge and is set to 0. Then a vertical projection (projecting on the Y-axis) of the edge detected image is taken and smoothed using an average filter with width equals 9. It’s obvious that the characters of the plate along with the plate’s vertical edges will have very strong vertical edges. Moreover, these edges will sum up horizontally in the vertical projection and a strong peak will appear in the rows of the plate (These row will be called band). So, the approach is to take some number of peaks in the vertical projection and processing each of them individually in the next steps and when a successful band is found, the processing of the following bands is cancelled. The reason behind taking more than one peak is that the image may contain objects (logos, road advertisement, etc..) that produce many vertical edges also these ”false” edges may be centred in the same area so they will form a peak that may be stronger than the peak of the plate itself. For each band, we take a sub-image referenced by this band and all subsequent processing will be applied on this sub-image. Now the problem is to cut the band image from the left and right to get a bounding rectangle over the license plate (Again, this rectangle doesn’t have to be tight on the plate). For this sake, a vertical Sobel edge detection is applied again, but the height is larger than the width of the filter, this is to decrease the effect of false edges and noise, experimentally, the best size is 6x3 filter .
  11. 11. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 11 Figure 3.2: Large bounding rectangle over the license plate-2 Figure 3.3: Large bounding rectangle over the license plate-3 Again a threshold of 30 is applied for the same reason as before. Now, a horizontal projection of the edge detected band image is taken (projection on the X-axis) and smoothed using an average filter of large size this time, since there are gaps between the letters and the projection will have many peaks at the x coordinates where letters exist but it will drop down in the x coordinates of the gaps. So, smoothing it with average filter of large width will resolve this problem and many number of peaks will be converted to one wide peak that represents the range of the X-axis where the plate is located in that specific band we are working with. The width of the average filter is taken to be the height of the band. Relating the height of the band with the width of the average filter is very important since over- smoothing of the projection will merge the plate peak with the other main peaks in the band like the peak got from vehicle lamps for example (and it already explained why the width shouldn’t be very small). Now, a predefined number of peaks (It’s already explained why we take more than one candidate peak not just the strongest one) will be selected from the smoothed projection. For each peak, a sub-image is taken according to the range of current peak. So, the bounding rectangle of the license plate is located. This is will be the input to the next step. 3.2.2 Determining the exact location of the license plate: Using the sub-image from the last step which contains the license plate with some extra parts (if any), the following processing is applied to this sub-image. The license plate may be skewed because of the angle of the camera while image acquisition process. And it is very important to de-skew the plate to its original orientation, thus making the plate
  12. 12. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 12 Figure 3.4: Determining exact location of license plate-1 Figure 3.5: Determining exact location of license plate-2 aligned with the X and Y axes (The reason behind its importance will be clear below). So a Hough transform is applied to the horizontally edge detected image in order to find the shear parameters by which the image can be de-skewed to retrieve the standard orientation. After this operation we have a plate with its axes aligned with the X and Y axes. Then a Gaussian smoothing filter is applied to smooth the image and remove noise. This operation makes the characters of the plate bold and increases the characters area along with the effect of increasing contrast, and subsequently this will ease the process of segmentation and recognition afterward. All the above is considered a pre-processing for this step. Next, we aim at finding the exact band of the plate. In other words, the goal of this step is to cut the top and bottom extra parts of the previously cut rectangle (but this time the cut will be accurate because we have limited the area we are working with and moreover we de-skewed the plate). This is done using the same idea we used previously to get the plate band. It consists of applying Sobel vertical edge detection, then applying a threshold, then doing a vertical projection (projecting on the Y-axis), Then getting the strongest peak in this projection and cut the image accordingly using the range of this peak, thus cutting the exact plate band from the image and leaving the top and bottom extra parts . This time just the strongest peak is taken since we already limited the possibility that false edges appear when we cut a rectangle around the plate and we are sure that the vertical edges produced by the plate’s characters are summed up correctly in a limited number of rows due to the de-skew operation.
  13. 13. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 13 Figure 3.6: Determining exact location of license plate-3 We got rid of the top and bottom extra parts. But we still have extra parts from left and right that have to be cut to end up with an exact rectangle around the plate. So, a stamp filter is applied to the sub-image we got from the previous step. This filter is just a blurring followed by a soft threshold operation. Now the white colour will dominate the plate area . After this a horizontal projection is done then smoothed using average filter with width equals 40. Then we get the strongest peak from this projection. This peak corresponds to the plate range on the X-axis. So, a sub-image is cut using the peak range. In many cases when the colour of the vehicle is bright, the previous operation is not sufficient to cut all the extra pieces from left and right. So, this is followed by getting Sobel horizontal edge detection, applying a threshold, then getting the horizontal projection, then smoothing this projection with average filter of size 40. Then we will get two points that will define range of the peak. The first point is the point with least x coordinate that has a value (from the smoothed projection) greater than or equal the average value. The second point is the point with maximum x coordinate that has a value greater than or equal to the average. We will cut the image again using these two points we got. And this is the final plate that the next processing stages will work on. At the current moment we have a ”candidate” final plate. The next processing stages are computationally expensive. Also using the fact that all the plates have a very similar (if not exact) values for some measures like aspect ratio, contrast, average brightness, average saturation in both the coloured and gray scale plate images. We can begin to reject the plates based on the previous measures, such that, If we found that the current candidate plate for any measure has a very far value from the ranges of values for the true plates, It’s simply rejected and the processing continues on the next candidate plate. But a false plate may pass these tests, and it will be rejected in subsequent stages. The next stage is to segment characters from the plate that passed all the measures tests.
  14. 14. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 14 3.3 Character Segmentation: This stage is meant for segmentation of the characters from the plate. The output of this stage is a set of monochrome images for each candidate character in plate. Figure 3.7: Character Segmentation-1 The first step in this stage is to convert the plate image to a binary image. This is done using adaptive threshold with a window of size 11 (This is selected using trial and error). Then a process of noise removal is applied. This is done by getting the connected components from the binary image based on the 8-neighbourhood using flood fill. For every component, we decide if it’s a noise or not based on the aspect ratio of the component and based on the number of pixels in that component. This is based on the fact that the characters of the plate have a certain range of aspect ratio and a certain range of number of pixels. After removing the noise components a maximum filter is applied to make the effect of thinning the characters to make sure that no two components are merged. This is followed by a horizontal projection, to detect the boundaries between the characters to be able to cut them individually. The peaks in this projection correspond to the gaps between the characters. So, we get all of these peaks and a rejection process is applied also, since a true plate has a fixed range of gaps between characters. So, any plate that has number of peaks that do not fit in that range, will be rejected. Also, there is a powerful rejection measure; it is the variance of the characters width (the variance of the spaces between peaks). After this the characters are cut according to the peaks of the previous projection. Then another set of measures are computed to reject the false characters that may still exist after the noise removal operation. These measures are aspect ratio, deviation from average height test, deviation from average contrast, deviation from average brightness, deviation from hue, deviation from average saturation. After rejecting false characters, if the number of characters is not located in a predefined range, then the plate is rejected. Otherwise, the processing is continued.
  15. 15. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 15 Figure 3.8: Character Segmentation-2 and for every character a copy of its corresponding location in the gray scale is got. The gray level histogram is computed for the sub-image of each character, This gray level histogram will have a standard shape which is one peak at the dark values (this corresponds to the character’s pixels) and another peak at the bright values (this corresponds to the background) and some small values between them. So, this gray level image is converted to binary using the following procedure. First, we find two peaks in the histogram then we find the minimum value in between, this will be the value of the threshold (thus, every pixel that has a gray level value less than the mentioned value, will be converted to black, every other value will be converted to white). This way for converting the gray scale image that contains only a character to binary one proved to be effective. At this point we have a set of binary images each contains one character and this is the output of this stage and the input to the next. 3.4 Character recognition: The goal of this stage is to recognize and classify the binary images that contain characters received from the previous one. After this stage every character must have a label and an error factor, and this error factor if greater than a predefined value will be used to reject false characters accidently passed from the previous steps. For the sake of classification, some features must be collected from the characters. The feature we work with in this system is the chain code of the contour of the image after dividing it into four tracks then into four sectors.
  16. 16. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 16 Figure 3.9: Character Recognition Also we used a feed forward artificial neural network trained with back propagation with sigmoid activation function and the ANN is trained on the chain code feature of the optimal characters images. The neural network has 4X4X8=128 input neuron, it also has 37 output neurons corresponds to the Arabic alpha-numeric set of characters except zero, it also ceil (37+128)/2)=83 hidden neurons. So, for every character we get the chain code feature and do a feed forward on the trained FFNN (Feed Forward Neural Network) then the class the corresponds to the neuron with the maximum value will the predicted class of that character. If the error exceeds a predefined value then the character is considered a false one and rejected. The plate is known to have a fixed range of characters that may appear in it, so if the total number of passed characters does not match this range, then the plate is rejected. Otherwise, the license plate number is found.
  17. 17. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 17 CHAPTER 4 TYPES OF ALPR SYSTEMS 4.1 Mobile ALPR Systems Mobile ALPR systems use vehicle-mounted cameras to capture license plate data and can be configured in a number of ways to meet specific agency needs. Typically, the processor is located in the trunk of the vehicle and the data is processed locally to notify the officer of a possible hit. ALPR cameras are affixed to a vehicle and can be either hardwired or magnet mounted for a portable (vehicle to vehicle) configuration. They can be integrated into the light bar, mounted on either the roof or trunk of the vehicle, or within covert housing.
  18. 18. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 18 Figure 4.1: Light bar (left) and Covert (right) Mounted Mobile ALPR Cameras System Portability: Mobile ALPR systems can be hardwired to a vehicle or magnet-mounted, for a portable (vehicle to vehicle) configuration. Magnet-mounted set-ups offer more flexibility and allow the agency to relocate the system from one vehicle to another. Consideration should be given, however, to the location of the hardware and connection cables on the various vehicle models and the ease with which they can be transferred. Agencies should weigh the pros and cons of each configuration against the technical and personnel resources of their agency. Vehicle space availability: Mobile ALPR system components include cameras, processors, an interface screen, and keyboard which need to be added to a vehicle. Consideration should be given to the existing space limitations in both the vehicle cockpit and trunk. Number of Cameras: Each camera added to the ALPR system on a vehicle provides an additional field of view and increases the amount of data and images the processor must analyse. Data Transfer: A variety of methods exist to transfer hot list and ALPR data from the vehicle’s computer processor. Consideration should be given to whether the agency plans to manually transfer the hot lists and data files using a USB memory stick or automate the transfer using wireless or cellular networks. 4.2 Fixed and Portable ALPR Systems ALPR cameras that are permanently affixed to a structure such as a light pole, bridge or overhead sign.
  19. 19. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 19 Figure 4.2: Stationary/Fixed ALPR Cameras on a Bridge and Utility Pole Fixed and portable ALPR systems require an installation design plan that includes infrastructure to support the camera system. This infrastructure includes power for the system and any networking that provides the ability to transmit data between the camera and the command/information centre. Some common considerations for fixed systems are: • Existing physical infrastructure • Site location • Available power • Available network infrastructure • Number of cameras • Dispatch requirements Existing Physical Infrastructure: A great deal of physical infrastructure already exists at key locations along roadways or potential targets (e.g. sports stadium or power plant). Utilizing established infrastructure can offer a number of advantages such as reduction in costs associated with setting up a site, ease of access, and existing power connections. Consideration should be given however to the agency responsible for the infrastructure as special permits and ongoing maintenance may be required. Site location: When choosing site locations for fixed and portable ALPR units, consideration should be given to whether officers will be routinely stationed nearby and their possible response times.
  20. 20. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 20 Available Power: Fixed and portable systems require power at the location of the camera. The need for power may limit the possible locations for mounting or require additional resources. Available network infrastructure: Fixed and portable systems require network connectivity between the ALPR system’s computer processor (generally located with the camera) and the server receiving database updates. The updates enable the processors at the camera location to identify vehicles of interest that have been recently entered into the databases. Agencies should consider how this network connectivity will be accomplished to ensure successful updates are received and how the information will be secured. Dispatch requirements: Fixed and portable systems typically provide alert notifications to the communications/operations centre. This increases the workload for the dispatch personnel. Depending on the system configuration, the ALPR system may require an additional computer screen for the dispatcher to monitor. Dispatch personnel need to be effectively trained and be able to include the associated actions into their existing responsibilities. It is also important to ensure that the dispatch facility has sufficient power and space for any additional computers or servers the ALPR system may require. Number of cameras: A fixed system typically requires the installation of one camera for each lane of traffic being monitored. Multiple cameras at one location may improve the ability to locate a suspect or wanted vehicle. CHAPTER 5 ALPR PERFORMANCE AND POLICIES A number of factors impact the performance of ALPR systems, and there are several measures that are relevant to the overall performance of the technology. 1. Capture Efficacy – a measure of the effectiveness of ALPR units to capture the license plate information of vehicles that pass through the field of view of ALPR cameras. For example, if 100 cars pass the ALPR unit, what proportion/percentage of vehicles containing license plates are actually captured (i.e., read) by the ALPR units?
  21. 21. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 21 2. Read Accuracy – a measure of the accuracy of ALPR system interpretation of captured plates with the actual alphanumeric characters of the plate. 3. Matching Effectiveness – a measure of the effectiveness of ALPR units (really of their underlying software matching algorithms) to accurately match license plates reads to records contained in the agency’s hot list(s). For example, if the ALPR unit accurately captures or reads only a portion of a vehicle’s plate, or misreads one or more characters on a vehicle plate, is the unit (and its software) nevertheless able to match the plate with hotlist records stored or accessed through the device (perhaps with a scoring factor related to the probability of an actual match)? This is more a function of the software supporting the ALPR unit, the calibration of matching algorithms, and a measure of the performance and elasticity of search parameters. 4. Capture/Read Factors – there are a host of factors that may influence the ability of ALPR units to capture and accurately read and match license plates. Capture/Read factors include the following: a. Character and/or plate colour b. Plate design factors (logos, stacked characters, etc.) c. State of origin (i.e., the state which issued the plate) d. Plate covers or other obstructions (e.g., bent, dirty, trailer hitch obstruction, etc.) e. Plate location on the vehicle f. Interval between vehicles g. Vehicle speed h. Lighting conditions (e.g., day vs. night) i. Weather conditions (e.g., snow, rain, fog) j. ALPR equipment (e.g., age and/or ability of the ALPR camera) k. ALPR implementation (e.g., camera angle) 5. Plate design: Each state has multiple license plate designs and plates vary substantially from state to state. The shape of the characters, amount of contrast between a particular state’s background and the colour of the license plate characters, and whether the characters are raised or flat can all impact the accuracy of the OCR read. Some colours, especially reddish tones, may be difficult for ALPR system OCR software to read.
  22. 22. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 22 Figure 5.1: Sample Plate Designs Poor image resolution: Poor image resolution can result from several factors. License plates can be too far away for the capabilities of the ALPR camera to capture and motion blur can also occur. Poor lighting and low contrast due to overexposure, reflection, adverse weather conditions, or shadows can also result in a poor image quality. Figure 5.2: Poor Image Quality Bent, dirty, damaged, or modified plates: Because many ALPR systems use reflectivity and the contrast created by the alphanumeric characters, plates that are bent, dirty, damaged, or modified may cause the ALPR software to misidentify a character. Figure 5.3: Bent, Dirty, or Damaged Plates Plate location: Occasionally, an object might obscure all or a portion of the license plate and interfere with accurate OCR. Oftentimes the object is a tow bar, dirt on the license plate, or a loaded
  23. 23. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 23 bike rack; other times the object may be a ALPR circumvention device. The National Policing Improvement Agency (NPIA) and the Association of Chief Police Officers (ACPO) have specified minimum performance capabilities of ALPR technologies in capturing and reading license plate information for UK and Schengen Community plates. Figure 5.4: Obstructed Plates Table 5.1: ‘Capture’ and ‘Read’ rates for All United Kingdom (UK) Type of System Capture Rate Correct Read Rate Overall capture & correct read rate Static ANPR Camera 98% 95% 93.1% CCTV Integrated ANPR (Dual purpose CCTV and ANPR Camera) 85% 85% 72.0% Mobile ANPR Camera (Stationary) 98% 95% 93.1% Mobile ANPR Camera (Moving) 80% 85% 68.0% Given the fact that in static ALPR implementations the camera capturing the license plate is stationary, whereas mobile implementations involve mobile cameras and potentially mobile target vehicles, variations in capture efficacy and read accuracy rates are expected and observed. Slightly lower performance rates are acceptable for capturing and reading plates of just Schengen member countries. Table 5.2: ‘Capture’ and ‘Read’rates of Schengen Community Type of System Capture Rate Correct Read Rate Overall capture & correct read rate
  24. 24. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 24 Static ANPR Camera 85% 80% 68.0% CCTV Integrated ANPR (Dual purpose CCTV and ANPR Camera) 85% 80% 68.0% Mobile ANPR Camera (Stationary) 85% 80% 68.0% Mobile ANPR Camera (Moving) 75% 80% 60.0% Law enforcement agencies in the United States must deal with license plates from other states, as well as international jurisdictions, which can pose difficulties if the units are not configured to “read” plates from multiple jurisdictions. ALPR manufacturers are constantly upgrading their equipment and software to address this issue. Minimum performance standards for ALPR in the United States are still at an early stage. IACP is managing an NIJ- funded project to develop technical performance standards for ALPR systems. The goals of the project are to a) identify the specific performance parameters that are critical to ALPR functions, b) develop metrics to accurately measure their performance, and c) establish protocols for the testing of the equipment by an independent laboratory. 5.2 ALPR Policies: In order to be effective, ALPR technology must be properly implemented and integrated into the daily operations of law enforcement agencies. Developing and enforcing policies defining the strategic objectives of an agency’s program, training requirements, deployment options, operating procedures, hot list management, proper use and maintenance of the technology, and data collection, retention, sharing, and access enables law enforcement to effectively manage ALPR. Agency policies typically: 1. Define the ALPR system and its data as “for official use only” (FOUO), 2. Restrict and audit queries of the ALPR dataset, and
  25. 25. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 25 3. Require that all operators of the equipment receive proper training before use. Essential components of that training include: 1. Ensuring that appropriate hot list information is as current and accurate as possible, and 2. Clear directions that when the ALPR unit matches an observed vehicle’s license plate with a record in the hot list and alerts the officer (also known as a “hit”), that the officer must verify a. that the ALPR “read” was accurate (i.e., that the ALPR OCR software has properly interpreted the license plate number), b. that the issuing State matches the plate on record, and c. that the circumstance that triggered the alert is still current, e.g., that the vehicle is still wanted or stolen. 3. If the record in the hot list was created based, not on the status of the vehicle, but rather on the status of the registered owner (e.g., the owner has an outstanding warrant for arrest, or has had their driving privileges suspended or revoked), the officer must also be cognizant of the fact that the driver may not be the registered owner. Additionally, depending on the nature of the alert (e.g., a “hit” on the Terrorist Watch List), the officer may be directed to notify another agency (e.g., the Terrorist Screening Centre) and hold the person, surreptitiously watch but not contact the person, or simply document the contact and forward the information to others. Nearly half of responding agencies (19 agencies, 48%) indicated that they had a policy addressing ALPR use and operations, and six agencies (15%) noted they were in the process of developing or planning one. Among agencies that have or are developing ALPR policies, the policies usually address data access (68%), data retention (48%), and data sharing (44%). Table 5.3: Policy Issues Addressed by Agencies Policy Issues n % Data access 17 68% Data retention 12 48% Data sharing 11 44%
  26. 26. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 26 Deployment 6 24% Data quality 4 16% Other 4 16% CHAPTER 6 ADVANTAGES OF USING ANPR TECHNOLOGY
  27. 27. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 27 With the help of this technology, pre-paid parking members can be easily differentiated from non-members. With the ability to capture the driver’s image, car hijacking can be prevented to a large extent. By having a list of stolen cars and unpaid fines in the database, identification of cars with such history can be easily made and real time alerts can be obtained, as soon as the car is located on a highway or at a signal. Traffic congestions can be lowered, by diverting different vehicles into different lanes with the help of their entry permits. Those persons or vehicles whose presence is barred from entry can be easily identified. License Plate Recognition (LPR) technology uses image processing to identify vehicle license plates. The technology is rapid and able to identify and record a license plate number under most ordinary driving conditions, including when a car is moving at high speed and at night. It offers advantages in law enforcement, security and vehicle access. Traffic Enforcement License plate recognition technology is an advantage in several areas of traffic enforcement. For example, a license plate recognition device can be put at an intersection and record the license plate of a car that runs a red light. A citation can then be sent to the registered owner of the car. Similarly, an LPR device can be placed in an area where speeding is a common problem, and record the infraction for follow-through with a citation. Law Enforcement License plate recognition technology has several applications in areas of law enforcement. An LPR device can be mounted on a patrol car to record plates of passing cars. This information can then be compared with the law enforcement data base for vehicles associated with a crime. This is useful in Amber alerts, finding stolen vehicles and executing felony warrants. Vehicle Access An LPR system is also useful for vehicle access. On toll roads, these systems can be used to allow cars to pass through toll gates without stopping. The LPR records the plate of the car and associates the number with the registered owner. The bill can then be sent by mail. This technology also can be used in secure, gated locations. When a vehicle recorded in a database approaches a security gate, the system recognizes the license plate, and the gate opens automatically. This allows the driver to pass without interruption and eliminates the possibility of an entrance code being stolen.
  28. 28. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 28
  29. 29. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 29 DIFFICUTIES AND FUTURE SCOPE There are a number of possible difficulties that the software must be able to cope with. These include:  Poor file resolution, usually because the plate is too far away but sometimes resulting from the use of a low-quality camera.  Blurry images, particularly motion blur  Poor lighting and low contrast due to overexposure and reflection or shadows.  An object obscuring (part of) the plate, quite often a tow bar, or dirt on the plate.  Read license plates that are different at the front and the back because of towed trailers, campers, etc.  Vehicle lane changes in the camera's angle of view during license plate reading.  A different font, popular for vanity plates (some countries do not allow such plates, eliminating the problem).  Circumvention techniques  Lack of coordination between countries or states. Two cars from different countries or states can have the same number but different design of the plate. While some of these problems can be corrected within the software, it is primarily left to the hardware side of the system to work out solutions to these difficulties. Increasing the height of the camera may avoid problems with objects (such as other vehicles) obscuring the plate but introduces and increases other problems, such as the adjusting for the increased skew of the plate. On some cars, tow bars may obscure one or two characters of the license plate. Bikes on bike racks can also obscure the number plate, though in some countries and jurisdictions, such as Victoria, Australia, "bike plates" are supposed to befitted. Some small-scale systems allow for some errors in the license plate. When used for giving specific vehicles access to a barricaded area, the decision may be made to have an acceptable error rate of one character. This is because the likelihood of an unauthorized car having such a similar license plate is seen as quite small. However, this level of inaccuracy would not be acceptable in most applications of an ANPR system. Future scope:
  30. 30. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 30 In recent years, digitisation and IP-based communication networks have allowed Automatic Number Plate Recognition (ANPR) to achieve ever-greater utility. Digitisation and ANPR have grown alongside each other and it has been natural for clients to include ANPR when upgrading, mainly because by the time digitisation reached traffic enforcement clients had a more open mind towards computers and expected their enforcement units to do what their home PC was able to. From the manufacturer's perspective, our aim is to make the operational aspects of enforcement as easy as possible and adding ANPR software to our digital units was a natural step. 25-30 percent growth: The ANPR market will grow by around 25-30 per cent in the next five years. Enforcement solutions combining, for example, speed measurement, camera and ANPR will drive future growth, according to ErnoSzucs of ARH, Inc. Electronic identification of vehicles using DSRC onboard units will always require some complementary, non-intrusive technique such as ANPR. The reason is obvious: assuring road administrations that every vehicle can be identified electronically would require complete agreement between all countries and vehicle manufacturers. That's far from being possible at the moment. CHAPTER 8
  31. 31. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 31 APPLICATIONS OF ANPR Automatic Number Plate Recognition has a wide range of applications since the license number is the primary, most widely accepted, human readable, mandatory identifier of motor vehicles. ANPR provides automated access of the content of the number plate for computer systems managing databases and processing information of vehicle movements. Below we indicated some of the major applications, without the demand of completeness. Parking: One of the main applications of ANPR is parking automation and parking security: ticketless parking fee management, parking access automation, vehicle location guidance, car theft prevention, "lost ticket" fraud, fraud by changing tickets, simplified, partially or fully automated payment process, among many others. Access Control: Access control in general is a mechanism for limiting access to areas and resources based on users' identities and their membership in various predefined groups. Access to limited zones, however, may also be managed based on the accessing vehicles alone, or together with personal identity. License plate recognition brings automation of vehicle access control management, providing increased security, car pool management for logistics, security guide assistance, event logging, event management, keeping access diary, possibilities for analysis and data mining. MotorwayRoadTolling: Road Tolling means, that motorists pay directly for the usage of particular segment of road infrastructures. Tolls are a common way of funding the improvements of highways, motorways, roads and bridges: tolls are fees for services. Efficient road tolling increases the level of related road services by reducing travel time overhead, congestion and improve roadways quality. Also, efficient road tolling reduces fraud related to non-payment, makes charging effective, reduces required manpower to process events of exceptions. License plate recognition is mostly used as a very efficient enforcement tool, while there are road tolling systems based solely on license plate recognition too. Border Control: Border Control is an established state-coordinated effort to achieve operational control of the country's state border with the priority mission of supporting the homeland's security against terrorism, illegal cross border traffic, smuggling and criminal activities.
  32. 32. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 32 Efficient border control significantly decreases the rate of violent crime and increases the society's security. Automatic number plate recognition adds significant value by event logging, establishing investigate-able databases of border crossings, alarming on suspicious passing’s, at many more. Journey Time Measurement: Journey Time Measurement is a very efficient and widely usable method of understanding traffic, detecting conspicuous situations and events, etc. A computer vision based system has its well known downfalls in Journey Time Measurement, while Automatic Number Plate Recognition has provided its viability: vehicle journey times can be measured reliably by automatic number plate recognition-based systems. Data collected by license plate recognition systems can be used in many ways after processing: feeding back information to road users to increase traffic security, helping efficient law enforcement, optimising traffic routes, reducing costs and time, etc. Law Enforcement: Automatic number plate recognition is an ideal technology to be used for law enforcement purposes. It is able to automatically identify stolen cars based on the up-to date blacklist. Other very common law enforcement applications are red-light enforcement and over speed charging and bus lane control.
  33. 33. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 33 CHAPTER 9 CONCLUSION The objective of this paper was to study and resolve algorithmic and mathematical aspects of the automatic number plate recognition systems, such as problematic of machine vision, pattern recognition, OCR and neural networks. The problematic has been divided into several chapters, according to a logical sequence of the individual recognition steps. Even though there is a strong succession of algorithms applied during the recognition process, chapters can be studied independently. Law enforcement agencies throughout the nation are increasingly adopting automated license plate recognition (ALPR) technologies to enhance their enforcement and investigative capabilities, expand their collection of relevant data, and expedite the tedious and time consuming process of manually comparing vehicle license plates with lists of stolen, wanted, and other vehicles of interest. Police officers, sheriff’s deputies, and other law enforcement practitioners are often on the lookout for vehicles that have been reported stolen, are wanted in connection with a crime or traffic violation, are suspected of being involved in criminal or terrorist activities, are parking violation scofflaws, have failed to maintain current registration or to comply with statutory insurance requirements, or any of a number of other legitimate reasons. ALPR systems function to automatically capture an image of the vehicle’s license plate, transform that image into alphanumeric characters using optical character recognition or similar software, compare the plate number acquired to one or more databases of vehicles of interest to law enforcement and other agencies, and to alert the officer when a vehicle of interest has been observed. ANPR solution has been tested on static snapshots of vehicles, which has been divided into several sets according to difficultness. Sets of blurry and skewed snapshots give worse recognition rates than a set of snapshots which has been captured clearly. The objective of the tests was not to find a one hundred percent recognizable set of snapshots, but to test the invariance of the algorithms on random snapshots systematically classified to the sets according to their properties.
  34. 34. A Technical Seminar Report on Underwater Communication Keshav Memorial Institute of Technology 34 BIBLIOGRAPHY [1] Shan Du ; IntelliView Technol., Inc., Calgary, AB, Canada ; Ibrahim, M. ; Shehata, M. ; Badawy, Wael; Automatic License Plate Recognition (ALPR): A State-of-the-Art Review". IEEE. 1 Feb 2013. Retrieved 2014-01-09. [2] C.N.E. Anagnostopoulos, I.E. Anagnostopoulos, I.D. Psoroulas, V. Loumos and E. Kayafas, License Plate Recognition From Still Images and Video Sequences: A Survey, IEEE Transactions on Intelligent Transportation Systems 9 (2008), no. 3, 377–391. [3] C.N.E. Anagnostopoulos, I.E. Anagnostopoulos, V. Loumos and E. Kayafas, A License PlateRecognition Algorithm for Intelligent Transportation System Applications, IEEE Transactions on Intelligent Transportation Systems 7 (2006), no. 3, 377–392. [4] S.-L. Chang; L.-S. Chen; Y.-C. Chung; S.-W. Chen, Automatic License Plate Recognition, IEEE Transactions on Intelligent Transportation Systems 5 (2004), no. 1, 42–53. [5] S. Draghici, A neural network based artificial vision system for licence plate recognition, International Journal of Neural Systems 8 (1997), no. 1, 113–126. [6] B. Enyedi, L. Konyha, C. Szombathy and K. Fazekas, Strategies for fast license plate number localization, Proceedings of the 46th International Symposium Electronics in Marine, Elmar 2004, Zadar, Croatia, June 16-18, 2004, IEEE Press (2004), 579–584 . [7] J.-W. Hsieh, S.-H. Yu and S.-H. Yu, Morphology-based License Plate Detection from Complex Scenes, IEEE Proceedings of the 16th International Conference on Pattern Recognition, Qubec City, Canada, August 11-15, 2002, Vol. 3 (2002), 176-179. [8] O. Martinsky, Algorithmic And Mathematical Principles Of Automatic Number Plate Recognition Systems, B.Sc. thesis, Department of Intelligent Systems, Faculty of Information Technology, Brno University of Technology (2007). [9] J.R. Parker and P. Federl, An Approach To Licence Plate Recognition, University of Calgary 1996. [10] H. Sarukhanyan, S. Alaverdyan, and G. Petrosyan, Automatic Number Plate Recognition System, Proceedings of the 7th International Conference on Computer Science and Information Technologies, Yerevan, Armenia, September 28-2 October, 2009, Electronic Copy of the CSIT 2009 Proceedings, 347-350.

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