Reduction of Video License Plate Data

2002 ◽  
Vol 1804 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Rajeev Gupta ◽  
Jon D. Fricker ◽  
David P. Moffett
Keyword(s):  
Data Reduction ◽  
Video Processing ◽  
Character Recognition ◽  
Reduction Process ◽  
Data File ◽  
Video Data ◽  
License Plate ◽  
Small Areas ◽  
Electronic File ◽  
License Plate Readers

Video license plate surveys have been used for more than a decade in Indiana to help produce origin-destination tables in corridors and small areas. In video license plate surveys, license plate images are captured on videotape for data reduction at the analyst’s office. In most cases, the letters and numbers on a license plate are manually transcribed to a data file. This manual process is tedious, time-consuming, and expensive. Although automated license plate readers are being implemented with success elsewhere, their dependence on high-end equipment makes them too expensive for most applications in Indiana. Presented are the results of an attempt to use standard video cameras and tapes, readily available video processing equipment, and open-source software to minimize the human role in the data reduction process and thus reduce the expenses involved. The process of automatically transcribing video data can be divided into subprocesses. Analog video data are digitized and stored on a computer hard disk. The resulting digital images are further processed, by using image-processing algorithms, to locate and extract the license plate and time stamp information. Character recognition techniques can then be applied to read the license plate number into an electronic file for the desired analysis. The described video license plate data reduction (VLPDR) software can identify video frames that contain vehicles and discard the remaining frames. VLPDR can locate and read the time stamps in most of these frames. Although VLPDR cannot read the license plate numbers into a data file, this final step is made easier by a user-friendly graphical user interface. VLPDR saves a significant amount of manual data reduction. The amount of labor saved depends on the parameters chosen by the user.

scholarly journals VIDEO COPY DETECTION UTILIZING THE LOG-POLAR TRANSFORMATION

2016 ◽  
pp. 8-13
Author(s):  
Daniel Reynolds ◽  
Richard A. Messner
Keyword(s):  
Data Reduction ◽  
Reduction Process ◽  
Video Data ◽  
Video Frame ◽  
Detection Accuracy ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Frame Size ◽  
Detection Process ◽  
Processing Step

Video copy detection is the process of comparing and analyzing videos to extract a measure of their similarity in order to determine if they are copies, modified versions, or completely different videos. With video frame sizes increasing rapidly, it is important to allow for a data reduction process to take place in order to achieve fast video comparisons. Further, detecting video streaming and storage of legal and illegal video data necessitates the fast and efficient implementation of video copy detection algorithms. In this paper some commonly used algorithms for video copy detection are implemented with the Log-Polar transformation being used as a pre-processing step to reduce the frame size prior to signature calculation. Two global based algorithms were chosen to validate the use of Log-Polar as an acceptable data reduction stage. The results of this research demonstrate that the addition of this pre-processing step significantly reduces the computation time of the overall video copy detection process while not significantly affecting the detection accuracy of the algorithm used for the detection process.

Application of Image Processing to the Vehicle License Plate Recognition

2013 ◽  
Vol 760-762 ◽  
pp. 1638-1641 ◽  
Author(s):  
Chun Yu Chen ◽  
Bao Zhi Cheng ◽  
Xin Chen ◽  
Fu Cheng Wang ◽  
Chen Zhang
Keyword(s):  
Traffic Engineering ◽  
Character Recognition ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Edge Extraction ◽  
Recognition Method ◽  
License Plate Location ◽  
Vehicle License Plate Recognition ◽  
Plate Location

At present, the traffic engineering and automation have developed, and the vehicle license plate recognition technology need get a corresponding improvement also. In case of identifying a car license picture, the principle of automatic license plate recognition is illustrated in this paper, and the processing is described in detail which includes the pre-processing, the edge extraction, the license plate location, the character segmentation, the character recognition. The program implementing recognition is edited by Matlab. The example result shows that the recognition method is feasible, and it can be put into practice.

A Feature Extracting Method for Chinese Character Recognition of License Plate Based on Elastic Mesh and Wavelet Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1452-1456
Author(s):  
Chao Zheng ◽  
Hua Yang ◽  
Xing Yang ◽  
Chao Chao Huang ◽  
Xiao Di Wu
Keyword(s):  
Wavelet Transform ◽  
Character Recognition ◽  
Chinese Character ◽  
Wavelet Coefficient ◽  
Difficult Problem ◽  
License Plate ◽  
Structure Information ◽  
Feature Extracting ◽  
Chinese Character Recognition ◽  
Character Feature

Low-resolution Chinese character recognition of license plate is always a difficult problem. For solving it, we must think about the distinctiveness of character feature and the counting speed of method simultaneously. In this paper, we proposed a simple and effective feature extraction algorithm. First, extract the statistical feature of Chinese character based on decomposing stroke with wavelet transform. Second, apply Elastic Mesh Algorithm into extracting wavelet coefficient of decomposing stroke to get the structure information of Chinese character. The experimental results show the method is robust against low quality Chinese characters, such as skew, fuzzy, glue, distorted character, and easy to be used in actual projects with simple advantage.

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

scholarly journals Residential Area Surveillance and Parking System

2020 ◽  
Vol 9 (5) ◽  
pp. 602-606
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Residential Area ◽  
High Rate ◽  
Identification System ◽  
License Plate ◽  
License Plate Recognition ◽  
Position Effects ◽  
Plain Text ◽  
Alert Message

The vehicles playing the vital role in our day to day life for transport, and some of the vehicles violates the traffic rules are also increasing, vehicle theft, unnecessary entering into highly restricted areas, increased number of accidents lead to increase in the rate of crime slowly. The vehicle had its own identity it should be recognized which plays the major role in the world. For recognition of the vehicles which are used commonly in the field of safety and security system, LPDR plays a major role and the vehicle registration number is recognized at some certain distance accurately. License Plate recognition is the most efficient and cost effective technique used for detection and recognition purposes. Automatic license plate recognition (ALPR) is used for finding the location of the license plate in the vehicle. These methods and techniques vary based on the conditions like, quality of the image, vehicle on a fine-tuned position, effects of lighting, type of image, etc. The objective is to design an efficient automatic conveyance identification system of sanctioned or unauthorized in the residential societies by utilizing the conveyance number plate. By getting the car image from the surveillance camera in the entrance, we recognizing the number plate and the characters are extracted using OCR (optical character recognition). It converts the character in the image to plain text. Then the plain text of the license plate is cross-verified with the database to check whether the vehicle belongs to residents or visitor. It sends the alert message to the security official when a new visitor request method in a residential area. The log details are stored separately for the resident and visitor in the database. It also provides the details about the parking area availability in the residential area. By calculating the number of vehicles in and out of the area, the detail or availability parking slot is displayed and it sis robust to the size, lighting effects with high rate of detection.

scholarly journals Intelligent Parking System

2019 ◽  
Vol 8 (2S3) ◽  
pp. 550-553
Keyword(s):  
Urban Communities ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Exit Time ◽  
Region Of Interest ◽  
License Plate ◽  
Past Study ◽  
Entry Time ◽  
Parking System ◽  
Best Fit

As a key part of Automated vehicle technology Intelligent Parking System has become a popular research topic. Intelligent Parking System can grant permission to access the parking area with less human inference. This system can capture image of the vehicle, identify the type of vehicle and allot best fit and optimal parking slot based on its size. It extracts the vehicle’s License plate number, entry time, exit time and calculate total time of the vehicle present with in the parking space. Here, sensors are utilized to identify the presence of the vehicle during entry and exit. Two cameras are utilized to extract features. One camera is used to identify the Region of Interest, Vehicle license plate and identify the characters from the license plate. Tesseract Engine and Optical Character Recognition (OCR) functions are used to detect characters from the image. Another camera is utilized to extract features like dimensions of the vehicle using machine learning operations such as Convolutional Neural Network (CNN). Based on the size of the vehicle, best fit parking slot is allotted which gives optimal usage of parking area. These days the quantity of vehicles is expanding exceptionally, so that, searching for an empty parking slot turns out to be increasingly troublesome. By installing the Intelligent Parking System, in places like, shopping malls, train stations, and airports the need for searching of parking slot significantly reduces. A past study has demonstrated that traffic because of vehicle’s parking slot searching in downtowns of significant urban communities can represent half of the absolute traffic. With such a hefty traffic jam and time delay in parking slot identifying, Intelligent Parking System will be in great demand

scholarly journals reductus: a stateless Python data reduction service with a browser front end

2018 ◽  
Vol 51 (5) ◽  
pp. 1500-1506 ◽  
Author(s):  
Brian Maranville ◽  
William Ratcliff II ◽  
Paul Kienzle
Keyword(s):  
Data Reduction ◽  
Open Data ◽  
Reduction Process ◽  
Flow Diagram ◽  
Data Repository ◽  
Accurate Estimation ◽  
Experimental Science ◽  
Online Data ◽  
Source Data ◽  
Estimation Of Uncertainties

The online data reduction service reductus transforms measurements in experimental science from laboratory coordinates into physically meaningful quantities with accurate estimation of uncertainties from instrumental settings and properties. This reduction process is based on a few well known transformations, but flexibility in the application of the transforms and algorithms supports flexibility in experiment design, enabling a broader range of measurements than a rigid reduction scheme for data. The user interface allows easy construction of arbitrary pipelines from well known data transforms using a visual data flow diagram. Source data are drawn from a networked, open data repository. The Python back end uses intelligent caching to store intermediate results of calculations for a highly responsive user experience. The reference implementation allows immediate reduction of measurements as they are recorded for the three neutron reflectometry instruments at the NIST Center for Neutron Research, without the need for visiting scientists to install additional software on their own computers.

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