Matching Vehicle License Plate Numbers Using License Plate Recognition and Text Mining Techniques

License plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring and access control. In most applications involving enforcement (e.g. cashless toll collection, congestion charging) and access control (e.g. car parking) a plate is recognized at one location (or checkpoint) and compared against a list of authorized vehicles. In this research I dealt with applications where a vehicle is detected at two locations and there is no list of reference for vehicle identification. There seems to be very little effort in the past to exploit all information generated by LPR systems. In nowadays, LPR machines have the ability to recognize most characters on the vehicle plates even under the harshest practical conditions. Therefore, even though the equipment are not perfect in terms of plate reading, it is still possible to judge with certain confidence if a pair of imperfect readings, in the form of sequenced characters (strings), most likely belong to the same vehicle. The challenge here is to design a matching procedure in order to decide whether or not they belong to same vehicle. In view of the aforementioned problem, this research intended to design and assess a matching procedure that takes advantage of a similarity measure called edit distance (ED) between two strings. The ED measure the minimum editing cost to convert a string to another. The study first attempted to assess a simple case of a dual LPR setup using the traditional ED formulation with 0 or 1 cost assignments (i.e. 0 if a pair-wise character is the same, and 1 otherwise). For this dual setup, this research has further proposed a symbol-based weight function using a probabilistic approach having as input parameters the conditional probability matrix of character association. As a result, this new formulation outperformed the original ED formulation. Lastly, the research sought to incorporate the passage time information into the procedure. With this, the performance of the matching procedure improved considerably resulting in a high positive matching rate and much lower (about 2%) false matching rate

Weighted Edit Distance for Country Code Recognition in License Plates

acceptedVersionPeer reviewe

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Video content analysis for intelligent forensics

The networks of surveillance cameras installed in public places and private territories continuously record video data with the aim of detecting and preventing unlawful activities. This enhances the importance of video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis. In this thesis, the primary focus is on four key aspects of video content analysis, namely; 1. Moving object detection and recognition, 2. Correction of colours in the video frames and recognition of colours of moving objects, 3. Make and model recognition of vehicles and identification of their type, 4. Detection and recognition of text information in outdoor scenes. To address the first issue, a framework is presented in the first part of the thesis that efficiently detects and recognizes moving objects in videos. The framework targets the problem of object detection in the presence of complex background. The object detection part of the framework relies on background modelling technique and a novel post processing step where the contours of the foreground regions (i.e. moving object) are refined by the classification of edge segments as belonging either to the background or to the foreground region. Further, a novel feature descriptor is devised for the classification of moving objects into humans, vehicles and background. The proposed feature descriptor captures the texture information present in the silhouette of foreground objects. To address the second issue, a framework for the correction and recognition of true colours of objects in videos is presented with novel noise reduction, colour enhancement and colour recognition stages. The colour recognition stage makes use of temporal information to reliably recognize the true colours of moving objects in multiple frames. The proposed framework is specifically designed to perform robustly on videos that have poor quality because of surrounding illumination, camera sensor imperfection and artefacts due to high compression. In the third part of the thesis, a framework for vehicle make and model recognition and type identification is presented. As a part of this work, a novel feature representation technique for distinctive representation of vehicle images has emerged. The feature representation technique uses dense feature description and mid-level feature encoding scheme to capture the texture in the frontal view of the vehicles. The proposed method is insensitive to minor in-plane rotation and skew within the image. The capability of the proposed framework can be enhanced to any number of vehicle classes without re-training. Another important contribution of this work is the publication of a comprehensive up to date dataset of vehicle images to support future research in this domain. The problem of text detection and recognition in images is addressed in the last part of the thesis. A novel technique is proposed that exploits the colour information in the image for the identification of text regions. Apart from detection, the colour information is also used to segment characters from the words. The recognition of identified characters is performed using shape features and supervised learning. Finally, a lexicon based alignment procedure is adopted to finalize the recognition of strings present in word images. Extensive experiments have been conducted on benchmark datasets to analyse the performance of proposed algorithms. The results show that the proposed moving object detection and recognition technique superseded well-know baseline techniques. The proposed framework for the correction and recognition of object colours in video frames achieved all the aforementioned goals. The performance analysis of the vehicle make and model recognition framework on multiple datasets has shown the strength and reliability of the technique when used within various scenarios. Finally, the experimental results for the text detection and recognition framework on benchmark datasets have revealed the potential of the proposed scheme for accurate detection and recognition of text in the wild

A methodology for using bluetooth to measure real-time work zone travel time

This thesis seeks to provide guidance on the deployment of Bluetooth sensors for travel time measurement in work zone corridors. The investigation focuses on the detection characteristics of Class 1 and Class 2 Bluetooth devices, and how cultivating an understanding of these characteristics together with the effect of the sensor inquiry cycle length can suggest a more precise method of travel time measurement. This thesis also explores the range of detection location around a Bluetooth sensor in order to recommend a minimum corridor separation of Bluetooth sensors, and to ascertain the best method of Bluetooth travel time derivation. Finally, this thesis investigates these principles further through multiple side-fire deployments on the I-285 corridor in Atlanta, Georgia; as well as two deployments capturing several hours of active work zone travel time.MSCommittee Chair: Dr. Michael Hunter; Committee Member: Dr. Angshuman Guin; Committee Member: Dr. Randall Guensle