Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.

Changing Road Signs in Israel: Production and Perception

In Palestine/Israel the struggle to control the land and the people is not merely conducted through physical violence. More subtle attempts for controlling the region and labeling it as belonging for one side rather than the other are implemented. This paper focuses on an Israeli suggestion to change the orthography of city names on road signs so that they are transliterations of the Hebrew name of the city. This one event, the Israeli suggestion to change city names on road signs, is represented to the public by two competing, and mostly opposing, discourses. This paper uses critical discourse analysis to analyze four articles, two of which are written by Arabic media sources, and the other two are written by Israeli ones. This analysis is paired with a quantitative and a qualitative analysis of the reactions of participants of different political affiliations to chosen excerpts of the articles. The paper aims at showing how one event is represented differently through different discourses, and how people who are affected be specific discourses react to them.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.

Color detection and segmentation for road and traffic signs

This paper aims to present three new methods for color detection and segmentation of road signs. The images are taken by a digital camera mounted in a car. The RGB images are converted into IHLS color space, and new methods are applied to extract the colors of the road signs under consideration. The methods are tested on hundreds of outdoor images in different light conditions, and they show high robustness. This project is part of the research taking place in Dalarna University / Sweden in the field of the ITS.

The impact of co-locating regulatory and directional signs on driver performance

The use of intelligent transport systems is proliferating across the Australian road network, particularly on major freeways. New technology allows a greater range of signs and messages to be displayed to drivers. While there has been a long history of human factors analyses of signage, no evaluation has been conducted on this novel, sometimes dynamic, signage or potential interactions when co-located. The purpose of this driving simulator study was to investigate drivers’ behavioural changes and comprehension resulting from the co-location of Lane Use Management Systems with static signs and (Enhanced) Variable Message Signs on Queensland motorways. A section of motorway was simulated, and nine scenarios were developed which presented a combination of signage cases across levels of driving task complexity. Two higher-risk road user groups were targeted for this research on an advanced driving simulator: older (65+ years, N=21) and younger (18-22 years, N=20) drivers. Changes in sign co-location and task complexity had small effect on driver comprehension of the signs and vehicle dynamics variables, including difference with the posted speed limit, headway, standard deviation of lane keeping and brake jerks. However, increasing the amount of information provided to drivers at a given location (by co-locating several signs) increased participants’ gaze duration on the signs. With co-location of signs and without added task complexity, a single gaze was over 2s for more than half of the population tested for both groups, and up to 6 seconds for some individuals.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

Road-sign identification using ensemble learning

Ensemble learning that combines the decisions of multiple weak classifiers to from an output, has recently emerged as an effective identification method. This paper presents a road-sign identification system based upon the ensemble learning approach. The system identifies the regions of interest that are extracted from the scene into the road-sign groups that they belong to. A large road-sign image dataset is formed and used to train and test the system. Fifteen groups of road signs are chosen for identification. Five experiments are performed and the results are presented and discussed.

The impact of presbyopic spectacles and contact lenses on driving performance

Presbyopia affects individuals from the age of 45 years onwards, resulting in difficulty in accurately focusing on near objects. There are many optical corrections available including spectacles or contact lenses that are designed to enable presbyopes to see clearly at both far and near distances. However, presbyopic vision corrections also disturb aspects of visual function under certain circumstances. The impact of these changes on activities of daily living such as driving are, however, poorly understood. Therefore, the aim of this study was to determine which aspects of driving performance might be affected by wearing different types of presbyopic vision corrections. In order to achieve this aim, three experiments were undertaken. The first experiment involved administration of a questionnaire to compare the subjective driving difficulties experienced when wearing a range of common presbyopic contact lens and spectacle corrections. The questionnaire was developed and piloted, and included a series of items regarding difficulties experienced while driving under day and night-time conditions. Two hundred and fifty five presbyopic patients responded to the questionnaire and were categorised into five groups, including those wearing no vision correction for driving (n = 50), bifocal spectacles (BIF, n = 54), progressive addition lenses spectacles (PAL, n = 50), monovision (MV, n = 53) and multifocal contact lenses (MTF CL, n = 48). Overall, ratings of satisfaction during daytime driving were relatively high for all correction types. However, MV and MTF CL wearers were significantly less satisfied with aspects of their vision during night-time than daytime driving, particularly with regard to disturbances from glare and haloes. Progressive addition lens wearers noticed more distortion of peripheral vision, while BIF wearers reported more difficulties with tasks requiring changes in focus and those who wore no vision correction for driving reported problems with intermediate and near tasks. Overall, the mean level of satisfaction for daytime driving was quite high for all of the groups (over 80%), with the BIF wearers being the least satisfied with their vision for driving. Conversely, at night, MTF CL wearers expressed the least satisfaction. Research into eye and head movements has become increasingly of interest in driving research as it provides a means of understanding how the driver responds to visual stimuli in traffic. Previous studies have found that wearing PAL can affect eye and head movement performance resulting in slower eye movement velocities and longer times to stabilize the gaze for fixation. These changes in eye and head movement patterns may have implications for driving safety, given that the visual tasks for driving include a range of dynamic search tasks. Therefore, the second study was designed to investigate the influence of different presbyopic corrections on driving-related eye and head movements under standardized laboratory-based conditions. Twenty presbyopes (mean age: 56.1 ± 5.7 years) who had no experience of wearing presbyopic vision corrections, apart from single vision reading spectacles, were recruited. Each participant wore five different types of vision correction: single vision distance lenses (SV), PAL, BIF, MV and MTF CL. For each visual condition, participants were required to view videotape recordings of traffic scenes, track a reference vehicle and identify a series of peripherally presented targets while their eye and head movements were recorded using the faceLAB® eye and head tracking system. Digital numerical display panels were also included as near visual stimuli (simulating the visual displays of a vehicle speedometer and radio). The results demonstrated that the path length of eye movements while viewing and responding to driving-related traffic scenes was significantly longer when wearing BIF and PAL than MV and MTF CL. The path length of head movements was greater with SV, BIF and PAL than MV and MTF CL. Target recognition was less accurate when the near stimulus was located at eccentricities inferiorly and to the left, rather than directly below the primary position of gaze, regardless of vision correction type. The third experiment aimed to investigate the real world driving performance of presbyopes while wearing different vision corrections measured on a closed-road circuit at night-time. Eye movements were recorded using the ASL Mobile Eye, eye tracking system (as the faceLAB® system proved to be impractical for use outside of the laboratory). Eleven participants (mean age: 57.25 ± 5.78 years) were fitted with four types of prescribed vision corrections (SV, PAL, MV and MTF CL). The measures of driving performance on the closed-road circuit included distance to sign recognition, near target recognition, peripheral light-emitting-diode (LED) recognition, low contrast road hazards recognition and avoidance, recognition of all the road signs, time to complete the course, and driving behaviours such as braking, accelerating, and cornering. The results demonstrated that driving performance at night was most affected by MTF CL compared to PAL, resulting in shorter distances to read signs, slower driving speeds, and longer times spent fixating road signs. Monovision resulted in worse performance in the task of distance to read a signs compared to SV and PAL. The SV condition resulted in significantly more errors made in interpreting information from in-vehicle devices, despite spending longer time fixating on these devices. Progressive addition lenses were ranked as the most preferred vision correction, while MTF CL were the least preferred vision correction for night-time driving. This thesis addressed the research question of how presbyopic vision corrections affect driving performance and the results of the three experiments demonstrated that the different types of presbyopic vision corrections (e.g. BIF, PAL, MV and MTF CL) can affect driving performance in different ways. Distance-related driving tasks showed reduced performance with MV and MTF CL, while tasks which involved viewing in-vehicle devices were significantly hampered by wearing SV corrections. Wearing spectacles such as SV, BIF and PAL induced greater eye and head movements in the simulated driving condition, however this did not directly translate to impaired performance on the closed- road circuit tasks. These findings are important for understanding the influence of presbyopic vision corrections on vision under real world driving conditions. They will also assist the eye care practitioner to understand and convey to patients the potential driving difficulties associated with wearing certain types of presbyopic vision corrections and accordingly to support them in the process of matching patients to optical corrections which meet their visual needs.

Effect of simulated visual impairment on nighttime driving performance

PURPOSE: This study investigated the effects of simulated visual impairment on nighttime driving performance and pedestrian recognition under real-road conditions. METHODS: Closed road nighttime driving performance was measured for 20 young visually normal participants (M = 27.5 +/- 6.1 years) under three visual conditions: normal vision, simulated cataracts, and refractive blur that were incorporated in modified goggles. The visual acuity levels for the cataract and blur conditions were matched for each participant. Driving measures included sign recognition, avoidance of low contrast road hazards, time to complete the course, and lane keeping. Pedestrian recognition was measured for pedestrians wearing either black clothing or black clothing with retroreflective markings on the moveable joints to create the perception of biological motion ("biomotion"). RESULTS: Simulated visual impairment significantly reduced participants' ability to recognize road signs, avoid road hazards, and increased the time taken to complete the driving course (p < 0.05); the effect was greatest for the cataract condition, even though the cataract and blur conditions were matched for visual acuity. Although visual impairment also significantly reduced the ability to recognize the pedestrian wearing black clothing, the pedestrian wearing "biomotion" was seen 80% of the time. CONCLUSIONS: Driving performance under nighttime conditions was significantly degraded by modest visual impairment; these effects were greatest for the cataract condition. Pedestrian recognition was greatly enhanced by marking limb joints in the pattern of "biomotion," which was relatively robust to the effects of visual impairment.

Differential effects of refractive blur on day and night-time driving performance

Purpose To investigate the effect of different levels of refractive blur on real-world driving performance measured under day and nighttime conditions. Methods Participants included 12 visually normal, young adults (mean age = 25.8 ± 5.2 years) who drove an instrumented research vehicle around a 4 km closed road circuit with three different levels of binocular spherical refractive blur (+0.50 diopter sphere [DS], +1.00 DS, +2.00 DS) compared with a baseline condition. The subjects wore optimal spherocylinder correction and the additional blur lenses were mounted in modified full-field goggles; the order of testing of the blur conditions was randomized. Driving performance was assessed in two different sessions under day and nighttime conditions and included measures of road signs recognized, hazard detection and avoidance, gap detection, lane-keeping, sign recognition distance, speed, and time to complete the course. Results Refractive blur and time of day had significant effects on driving performance (P < 0.05), where increasing blur and nighttime driving reduced performance on all driving tasks except gap judgment and lane keeping. There was also a significant interaction between blur and time of day (P < 0.05), such that the effects of blur were exacerbated under nighttime driving conditions; performance differences were evident even for +0.50 DS blur relative to baseline for some measures. Conclusions The effects of blur were greatest under nighttime conditions, even for levels of binocular refractive blur as low as +0.50 DS. These results emphasize the importance of accurate and up-to-date refractive correction of even low levels of refractive error when driving at night.

Ballykinler-Ballykinlar

Ballykinler / Ballykinlar, on the Co. Down coastline with the Mourne Mountains as a backdrop is a village of 250 inhabitants, beside a British army base with 1500 inhabitants. It is a complex situation with a troubled and contested history, reflected by the two different spellings on the village road signs. The village is viewed as having low levels of community capacity and longstanding sectarian problems.
PS2 has been working with the artist, Anne Marie Dillon and the local community of Ballykinler / Ballykinlar since April 2009, facilitating and curating a range of ‘live’ projects that address the lack of shared community and cultural space. A series of projects has modestly advanced the village’s ambition- realising alternative cultural centres.

1. Bus Stop (April 2009)
www.pssquared.org/Ballykinler.php
2. Rhyzom Network Workshop (June 2010)
www.pssquared.org/research.php
3. Beach Huts (March 2011)
www.pssquared.org/beachhuts.php
4. Up Down (May 2011) (included village fair)
www.pssquared.org/UP-Down.php villageworks.org.uk/wp/
5. Transfer Tests (March 2012)
www.pssquared.org/streesociety2012.php
6. Links Community Gardens (May 2012)
www.pssquared.org/LinksGardens.php