Soodamani Ramalingam

License plate localisation based on morphological operations

Automatic Number Plate Recognition (ANPR) systems allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. This paper presents an improved method to locate car plates in an ANPR system. The proposed method is based on morphological open and close operations where different Structuring Elements (SE) are used to maximally eliminate non-plate region and enhance plate region. This method has been tested using a database of UK number plates and results achieved have shown significant improvements in terms of the detection rate compare to other existing plate localisation systems.

Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems

This paper considers real world UK number plates and relates these to ANPR. It considers aspects of the relevant legislation and standards when applying them to real world number plates. The varied manufacturing techniques and varied specifications of component parts are also noted. The varied fixing methodologies and fixing locations are discussed as well as the impact on image capture.

Edge detection comparison for license plate detection

The detection of license plate region is the most important part of a vehicles license plate recognition process followed by plate segmentation and optical character recognition. Edge detection is commonly used in license plate detection as a preprocessing technique. This paper compares the performance of the image enhancement filters when used in edge detection algorithms combined with connected component analysis to extract license plate region. The experimental comparison of Canny, Kirsch, Rothwell, Sobel, Laplace and SUSAN edge detectors on gray scale images shows that Canny yields high plate detection of 98.2% tested on 45,032 UK images containing license plates at 720×288 resolution captured under various illumination conditions. The average processing time of one image is 56.4 ms.

Comparison of real-time DSP-based edge detection techniques for license plate detection

In this paper, edge detection techniques and their performance are compared when applied in license plate detection using an embedded digital signal processor. License plate detection remains to be the crucial part of a vehicles license plate recognition process. The edge detection algorithms compared in this work are those reported capable of delivering real-time performance. These are Canny-Deriche-FGL, Haar and Daubechies-4 wavelet transform and the classic Sobel. These particular algorithms are chosen and compared due to their good performance on digital signal processors. The comparison is drawn in terms of speed and detection success of a license plate. The results show Haar wavelet-based edge detector performs better on a DSP with LP detection speed of 7.32 ms and 98.6% success using 45,032 UK images containing license plates at 768×288 resolutions.

Real-time license plate localisation on FPGA

Automatic Number Plate Recognition (ANPR) systems have become an important tool to track stolen car, access control and monitor the traffic. The fundamental requirements of an ANPR system are image capture using an ANPR camera, and processing of the captured image. The image processing part, which is a computationally intensive task, includes two stages i.e. plate localisation and character recognition. This paper presents an improved license plate localisation (LPL) algorithm based on modified Sobel vertical edge detection operator and two morphological operations suitable for FPGA implementation. The algorithm has been successfully implemented on a Xilinx Virtex-4 FPGA and tested using a database of 1000 images that contains UK number plates. It consumes 28% of the available on-chip resources, runs with a maximum frequency of 114.20 MHz, has a detection rate of 99.1% and capable of processing one image (640×480) in 3.8ms.

Improved number plate localisation algorithm and its efficient field programmable gate arrays implementation

Number plate localisation is a very important stage in an automatic number plate recognition (ANPR) system and is computationally intensive. This study presents a low complexity with high-detection rate number plate localisation algorithm based on morphological operations together with an efficient multiplier-less architecture based on that algorithm. The proposed architecture has been successfully implemented and tested using a Mentor Graphics RC240 FPGA (field programmable gate arrays) development board equipped with a 4M-gate Xilinx Virtex-4 LX40. Two database sets sourced from the UK and Greece and including 1000 and 307 images, respectively, both with a resolution of 640 × 480, have been used for testing. Results achieved have shown that the proposed system can process an image in 4.7 ms, while achieving a 97.8% detection rate and consuming only 33% of the available area of the FPGA.

Real-Time DSP-Based License Plate Character Segmentation Algorithm Using 2D Haar Wavelet Transform

The potential applications of Wavelet Transform (WT) are limitless including image processing, audio compression and communication systems. In image processing, WT is used in applications such as image compression, denoising, speckle removal, feature analysis, edge detection and object detection. The use of WT algorithms in image processing for real-time custom applications may require dedicated processors such as Digital Signal Processor (DSPs), Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) as reported in (Ma et al., 2000), (Benkrid et al., 2001) and (Wong et al., 2007) respectively.

3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation.

A fuzzy interval valued fusion technique for multi-modal 3D face recognition

This paper proposes a fuzzy interval valued multicriteria decision making (MCDM) technique that aggregates information from multi-modal feature sets during decision making in a 3D face recognition system. In this paper, an interval valued fuzzy TOPSIS technique is applied to a 3D face recognition system that is benchmarked against a set of databases. Such a system is shown to be useful in decision making when the choice of alternatives of the feature sets is combinatorial and complex.

Impact of character spacing on the performance of Automatic Number Plate Recognition (ANPR) systems through simulation

Work on Automatic Number Plate Recognition (ANPR) as part of road safety by detecting and deterring a range of illegal road users is currently a key research work undertaken by the School of Engineering and Technology in collaboration with the UK Home Office1 and Hertfordshire Constabulary. A key research problem identified is the lack of an objective and independent assessment process for benchmarking ANPR systems in the UK. With this is mind, it is proposed to generate key data sets through a simulation process that will generate car number plate images. As a first step, such plates will show variability in character spacing for assessing ANPR systems which will demonstrate the principles for benchmarking. Such a system will avoid the need for carrying out any resource intensive field trials by the Police Force which is currently the case.