DESIGN AND PROTOTYPE IMPLEMENTATION OF AUTOMATIC PARKING SYSTEM

Parking system became a necessity for public places especially in big cities. For reasons of efficiency of human resources, the automatic parking system was developed. Automatic parking system is a parking system without any human intervention as a system operator. To create a fully automated system, requires a mechanism automatically recording the vehicle's identity and security mechanisms to prevent acts of theft. Vehicle plate number recognition is a method of image processing to recognize license plate number and vehicle color recognition as an additional method, to provide an additional vehicle identity. The use of two recognition methods to make the system work automatically. To add security features to the system multifactor authentication is applied. This paper presents a study, design and prototyping of automatic car park system. Where plate number recognition and color recognition are adopted to make full automatic system. The use of technologies such as RFID and microcontroller is needed in the future to implement real systems that is completely automated. The design of this system is expected to provide the efficiency of human resources. Keywords: automatic parking system, prototype, recognition, plate numbe

Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems

CAR NUMBER PLATE RECOGNITION

The project is about car number plate recognition. In this project, program needs to be developed tbat can automatically read the number plate of tbe vehicles. Car number plate recognition is an image processing technology used to identify vehicles by their plate [7]. It has been used widely in a number of applications such as automation car parking system, security parking system, and automated payment toll. In this project, image processing work will be done by using the image processing toolbox in MATLAB. In order to identify characters form tbe vehicle's plate, image processing techniques such as extraction, enhancement, and segmentation of certain features need to be implemented for identification of the characters

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

AUTOMATIC IRAQI CARS NUMBER PLATES EXTRACTION

License Plate Recognition (LPR) system becomes animportant research issue in recent years due to its importance to wideranges of commercial applications. The first and the most importantstage for any LPR system is the localization of the number platewithin the vehicle image. This paper presents a methodology for Iraqicars number plates extraction from the vehicle image using twomethods, the first one is morphological operations and the secondmethod is edge detection. The main idea is to use these two differentmethods in such away so that the number plate of the vehicle can beextracted precisely. These algorithms can quickly and correctly detectand extract the number plate from the vehicle image although therewas a little noise in the image. This paper also makes a comparisonbetween the two methods of extraction in results. The software thatused to build the systems is MATLAB R2014

Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing

Automatic detection, extraction and recognition of vehicle number plate region in traffic control systems is one of the prominent application in Computer vision. The drastic increase in number of vehicles in the current generation greatly increases the complexity in tracking the vehicles through the human visual system, manual procedure of controlling traffic and enforcement of various laws and rules is not sufficient for smooth control of traffic. This urges the need for development of technology that can automate this process. This paper mainly focuses on the development of an automatic number plate extraction and recognition algorithm by incorporating constructs like edge detection, horizontal and vertical edge processing using fixed threshold technique. The extracted number plate region is again processed using template matching algorithm for the recognition of the characters embossed on the number plate with respect to every individual piece of number plate. The algorithm developed has achieved an accuracy of around 100% and works for both front and rear images of the car

Certain Investigations on Vehicles Number Plate Identification using Top Hat Transform Algorithm and Optical Character Recognition

Investigation on vehicles number plate with top hat transforms is the method to recognize the characters on number plate utilizing the process like Image processing and OCR. The conception of this project is, first the image of the vehicle is to be captured. Next, the number plate of the vehicles is extracted from captured images using Top Hat transform algorithms. Conclusively, Optical Character Recognization recognizes the character presented in number plate. Additionally, the extracted data is stored in our database. This project can be implemented on various security zones like Parking Systems, Traffic Control areas, Toll gates, tracking of vehicles, etc. In the current scenario, the usage of vehicles increases day by day. Hence it's impossible to maintain the record manually for entire Vehicles. By expanding this system it becomes easy to sustain such rather records. In the majority of the nations, the extent of the number plate relies upon the aspect ratio. It can be figured by Width over Height. This work proposes the strategy for following Indian Number Plates of the vehicle. While contrasting other number plate extraction strategy this technique varies in such a path, in several strategies, they utilized just an area of a number plate for recognizing the character. However, in this method the entire vehicle can be included which first finds the particular zone of number plate then it executes character Recognition. Template matching technique where used in previous methods of number plate identification which one and only needs an area of a number plate. The disadvantages of previous techniques are it can only recognize already stored character in the templates and if there is more than one number plate, it is impossible to identify the sector of the number plate. Therefore to overcome such errors, we developed this algorithm which relatively gives better results while comparing with other methods. The absolute time taken for one execution is below 5 seconds

IMPLEMENTATION OF IMAGE PROCESSING IN THE RECOGNITION OF OFFICIAL VEHICLE LICENSE PLATES

Vehicle license plates are identifiers used to uniquely identify vehicles. However, to identify vehicle license plates there are several problems encountered, namely the different formats of vehicle license plates that make license plate recognition more complicated, vehicle license plates often contain visually similar combinations of letters and numbers (for example the letter "O" and the number "0" or the letter "I" and the number "1"), . in poor lighting conditions license plates may not be clearly visible. To solve this problem, image recognition, image processing, and pattern recognition technologies can be used. The three technologies can be used to recognize characters on vehicle license plates, but cannot yet be used to recognize the colors contained on vehicle license plates. The purpose of this research is to identify and record vehicle license plate numbers quickly and accurately, monitor the presence of vehicles in a supervised area, assist in managing parking, reduce the need for human interaction in the vehicle identification process, The methods used to recognize motor vehicle plates are edge detection and character segmentation which involves image processing to detect the edges of the vehicle plate, followed by segmentation of individual characters in the plate. Another method used is optical character recognition which involves using an optical sensor to take an image of a vehicle plate, then using character recognition techniques to identify the numbers and letters on the plate. The result of this research is that the motor vehicle number recognition system can work in various lighting conditions and poor weather conditions and can monitor and control vehicles in the parking area. The finding obtained from this research is that no method has been used for color recognition on motor vehicle plates