Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

An Intelligent Monitoring System of Vehicles on Highway Traffic

Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames. On the basis of these frames, a vehicle can be tracked by using radius growing method, and its speed can be calculated by calculating vehicle mask and its displacement in consecutive frames. The method uses pattern recognition, digital image processing and mathematical techniques for vehicle detection, tracking and speed calculation. The validity of the proposed model is proved by testing it on different highways.Comment: 5 page

Real Time Automatic Number Plate Recognition Using Morphological Algorithm

The rising increase of up to date urban and national road networks over the last three decades become known the need of capable monitoring and management of road traffic. Expected techniques for traffic measurements, such as inductive loops, sensors or EM microwave detectors, endure from sober shortcomings, luxurious to install, they demand traffic distraction during installation or maintenance, they are massive and they are unable to notice slow or momentary stop vehicles. On the divergent, systems that are based on video are simple to install, use the existing infrastructure of traffic observation. Currently most reliable method is through the detection of number plates, i.e., automatic number plate recognition (ANPR), which is also branded as automatic license plate recognition (ALPR), or radio frequency transponders. The first revalent step of information is finding of moving objects in video streams and background subtraction is a very accepted approach for foreground segmentation. Next step is License plate extraction which is an essential stage in license plate recognition for automatic transport system. We are planned for two ways for removal of license plates and comparing it with other existing methods. The Extracted license plates are segmented into particular characters by means of a region-based manner. The recognition scheme unites adaptive iterative thresholding with a template matching algorithm. The method is strong to illumination, character size and thickness, skew and small character breaks. The main reward of this system is its real-time capability and that it does not require any extra sensor input (e.g. from infrared sensors) except a video stream. This system is judged on a huge number of vehicle images and videos. The system is also computationally extremely efficient and it is appropriate for others related image recognition applications. This system has broad choice of applications such as access control, ringing, border patrol, traffic control, finding stolen cars, etc. Furthermore, this technology does not need any fitting on cars, such as transmitter or responder

Vehicle Speed Measurement and Number Plate Detection using Real Time Embedded System

A real time system is proposed to detect moving vehicles that violate the speed limit. A dedicated digital signal processing chip is used to exploit computationally inexpensive image-processing techniques over the video sequence captured from the fixed position video camera for estimating the speed of the moving vehicles. The moving vehicles are detected by analysing the binary image sequences that are constructed from the captured frames by employing the inter-frame difference or the background subtraction techniques. The detected moving vehicles are tracked to estimate their speeds.This project deals with the tracking and following of single object in a sequence of frames and the velocity of the object is determined. The proposed method varies from previous existing methods in tracking moving objects, velocity determination and number plate detection. From the binary image generated, the moving vehicle is tracked using image segmentation of the video frames. The segmentation process is done by using the thresholding and morphological operations on the video frames. The object is visualized and its centroid is calculated. The distance it moved between frame to frame is stored and using this velocity is calculated with the frame rate of video.The images of the speeding vehicles are further analysed to detect license plate image regions. The entire simulation is done in matlab and simulink simulation software. Keywords:morphological;thresholding;segmentation;centroi

Real-time search-free multiple license plate recognition via likelihood estimation of saliency

In this paper, we propose a novel search-free localization method based on 3-D Bayesian saliency estimation. This method uses a new 3-D object tracking algorithm which includes: object detection, shadow detection and removal, and object recognition based on Bayesian methods. The algorithm is tested over three image datasets with different levels of complexities, and the results are compared with those of benchmark methods in terms of speed and accuracy. Unlike most search-based license-plate extraction methods, our proposed 3-D Bayesian saliency algorithm has lower execution time (less than 60 ms), more accuracy, and it is a search-free algorithm which works in noisy backgrounds