A Generic Information and Consent Framework for the IoT

The Internet of Things (IoT) raises specific issues in terms of information and consent, which makes the implementation of the General Data Protection Regulation (GDPR) challenging in this context. In this report, we propose a generic framework for information and consent in the IoT which is protective both for data subjects and for data controllers. We present a high level description of the framework, illustrate its generality through several technical solutions and case studies, and sketch a prototype implementation

Traffic Congestion Pricing Methods and Technologies

This paper reviews the methods and technologies for congestion pricing of roads. Congestion tolls can be implemented at scales ranging from individual lanes on single links to national road networks. Tolls can be differentiated by time of day, road type and vehicle characteristics, and even set in real time according to current traffic conditions. Conventional toll booths have largely given way to electronic toll collection technologies. The main technology categories are roadside-only systems employing digital photography, tag and beacon systems that use short-range microwave technology, and in vehicle-only systems based on either satellite or cellular network communications. The best technology choice depends on the application. The rate at which congestion pricing is implemented, and its ultimate scope, will depend on what technology is used and on what other functions and services it can perform. Since congestion pricing calls for the greatest overall degree of toll differentiation, congestion pricing is likely to drive the technology choice.Road pricing; Congestion pricing; Electronic Toll Collection technology

IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

A Review of Automatic License Plate Recognition System in Mobile based Platform

Automatic license plate recognition (ALPR) is the process of retrieving license plate information from a captured image or video frames from a sequence of videos. ALPR can assist law enforcement officers to identify stolen vehicles or to capture vehicle information from those that violate traffic laws instantly. It is also commonly used as an electronic payment system for toll payment or parking fee payment. Traditionally, ALPR is installed in a PC-based platform to take advantage of its processing power to process high-quality images captured by high-resolution cameras. Most smartphones nowadays are equipped with a high-quality camera and faster processing system which can be used to develop portable ALPR system. Thus, this has encouraged many researchers to work on implementing ALPR technology for the mobile platform. In this paper, we reviewed several researches that have implemented ALPR in the mobile-based platform. We discuss the techniques used in the three main stages of ALPR namely localisation, segmentation and recognition. The advantages and disadvantages of each technique are summarised in a table

Model based methods for locating, enhancing and recognising low resolution objects in video

Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

Classification of sporting activities using smartphone accelerometers

In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach