Automatically detecting road sign text from natural scene video

Automatic detection of text on road signs can help drivers keep aware of the traffic situation and surrounding environments by reminding them of the signs ahead. Current systems can only detect constrained road signs or produce unsatisfying performance when dealing with complex scenes in practical use. This paper firstly reviews the existing techniques used for text detection from natural scene. A novel system which detects text on road signs from natural scene video is then proposed. Our detailed approaches and methodology give a promising solution to this problem in order to reduce the running time and improve the recognition rate. © 2006 IEEE

Smart Application for Every Car (SAEC). (AR Mobile Application)

Technology is continuously evolving at an exponential rate. Fast technological advances are being made, especially in the field of smart phones, that facilitate the conduct of our daily activities in many areas such as driving. The ever-increasing number of vehicles on roads increases the likelihood of traffic accidents, resulting in higher number of deaths and serious injuries to drivers, passengers, and pedestrians. Among the main causes of road accidents are over speeding, unsafe lane jumping, and failure to keep a safe distance between vehicles, to name a few. In an attempt to contribute to the improvement of road traffic safety, we have developed an Augmented Reality-based Smart Vehicle Driver Assistance application. The application is designed to enhance vehicle driver\u27s safety, in particular, but is also considered to lead to incremental improvement of safety of road traffic. The application can run on both Android and iOS platforms and incorporates several beneficial features required by a vehicle driver such as monitoring of vehicle speed, warning the driver in case of lane deviation, detection of road signs, and to alert the driver if the vehicle is not being driven at a safe distance from the vehicle in front. In addition to providing information to improve safe driving, the application also helps the vehicle driver save parking location of the vehicle in order to efficiently identify the parking location when retrieving the vehicle. This feature is very useful at large and unfamiliar parking areas, such as at airports or one-off large public gatherings, especially in inclement weather. The application also includes other useful functions such as the payment of parking fees, storage of information regarding vehicle maintenance, and keeping the vehicle legal document up to date. The application uses the stored information to display reminders of the appropriate action that needs to be taken before it becomes overdue

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Color Segmentation for Extracting Symbols and Characters of Road Sign Images

Abstract—This paper presents a color segmentation technique based on the normalized RGB chromaticity diagram for extracting symbols and characters of road sign images. The method separates blue color of the sign’s background by utilizing the developed histogram on the RGB chromaticity diagram for selecting threshold automatically. The morphology operators are used to extract symbols and characters. From the experiments using real scene images with varying illumination, the proposed method could extract symbols and characters of road sign images properly. Index Terms—Color segmentation, RGB chromaticity diagram, objects extraction, guidance sign

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e. achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.This work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339

A New Color Segmentation Method Based on Normalized RGB Chromaticity Diagram

Abstract - This paper presents the new color segmentation method based on normalized RGB chromaticity diagram by utilizing the line in the chromaticity diagram to separate colors. Parameter of the line is obtained automatically using the peak and valley analysis of the newly developed histograms. The method is simple, fast, and effective to overcome the problem of illumination changes. The method shows the promising results for color segmentation of the outdoor sign images. Keywords: color segmentation, normalized RGB, chromaticity diagram, sign recognitio