Visual search for traffic signs: the effects of clutter, luminance, and aging

Latency and eye movement measures were used to examine the effects of aging, clutter, and luminance on visual search for traffic signs embedded in digitized images of driving scenes. Initially 14 older and 14 younger observers classified daytime and nighttime traffic scenes as containing low or high amounts of clutter. Next, an independent sample of 14 younger and 14 older participants searched for traffic signs contained within these scenes. Errors were more common among the elderly. Search efficiency declined with increased clutter and with aging. However, relative to the young, older adults did not suffer disproportionately as a result of increased clutter. The methods developed might be profitably employed to assess sign conspicuity and sign acquisition during driving.     

Traffic signal color recognition is a problem for both protan and deutan color-vision deficients

We investigated the effect of color-vision deficiency on reaction times and accuracy of identification of traffic light signals. Participants were 20 color-normal and 49 color-deficient males, the latter divided into subgroups of different severity and type. Participants performed a tracking task. At random intervals, stimuli simulating standard traffic light signals were presented against a white background at 5 degrees to right or left. Participants identified stimulus color (red/yellow/green) by pressing an appropriate response button. Mean response times for color normals were 525, 410, and 450 ms for red, yellow, and green lights, respectively. For color deficients, response times to red lights increased with increase in severity of color deficiency, with deutans performing worse than protans of similar severity: response times of deuteranopes and protanopes were 53% and 35% longer than those of color normals. A similar pattern occurred for yellow lights, with deuteranopes and protanopes having increased response times of 85% and 53%, respectively. For green lights, response times of all groups were similar. Error rates showed patterns similar to those of response times. Contrary to previous studies, deutans performed much worse than protans of similar severity. Actual or potential applications of this research include traffic signal design and driver licensing.     

The role of peripheral vision in vertical road sign identification and discrimination

Abstract

The role of peripheral vision in road sign identification and discrimination was investigated in two studies. Peripheral vision plays an important role in road signs perception due to their lateral positioning. In the first study 20 participants identified road signs presented at five levels of horizontal eccentricity (1.1°–12.4°), and two levels of vertical eccentricity (0°–2.5°). In the second study road sign discrimination was tested in a same-different discrimination task. The first study showed that a vertical offset of 2.5° degraded proportion correct rate by 9%. Proportion correct rate decreased from 79% to 41% in the transition from 1.1° to 12.4° of horizontal offset. The second study showed an accurate discrimination for road signs presented within a horizontal offset of 6.4°. Road signs with angular shapes and prominent vertexes as triangular or cross signs were better identified in peripheral vision than signs with more compact shapes (circular signs).

Practitioner summary: Vertical road signs, due to their lateral positioning, are often perceived in peripheral vision. Horizontal and vertical eccentricity negatively impacts the driver’s ability to correctly identify and discriminate traffic signs. The use of singular shapes and a design with simple pictograms and large contrasting areas strongly facilitate road sign perception in peripheral vision.     

A simulated study on the effects of information volume on traffic signs, viewing strategies and sign familiarity upon driver''s visual search performance

A two-stage simulation experiment was conducted to investigate the effect of information volume on traffic regulatory/road direction signs, drivers’ viewing strategies and sign familiarity on performance in visual search. In Stage I experiment, the amount of information on a total of 187 traffic regulatory and 1272 road direction signs was calculated and divided into five information levels using cluster analysis. In Stage II experiment, 24 subjects participated in a 2 (familiarity)×5 (information volume level)×2 (viewing strategy) mixed factorial experiment. Each subject was required to perform a visual search task and a question-and-answer (Q&A) task. Visual search time and number of correct responses collected serve as the objective dependent variables. Subjective workload related to time stress and visual effort was gathered through a modified three-point rating. Results show that information volume on traffic signs had significant impact on drivers’ visual search performance. Generally, the greater the amount of information, the slower the drivers in visual search are. However, while drivers had the highest accuracy rate in remembering purely pictorial traffic signs, these signs within the smallest information volume level required a relatively longer search time. Different viewing strategies also led to different performances. The back-and-forth strategy yielded better search performance than the fixed strategy. Subjective workload evaluation indicates that drivers with less sign familiarity will be under greater time/visual pressures. Guidelines for designing for traffic signs or in-vehicle signing systems are provided.

Relevance to industry

This study calculated the amount of information on traffic signs and compared the effects of different information volume on drivers’ visual search performance. The results can provide guidelines for traffic sign designers to help decide on the presentation format for in-vehicle signing information systems.     

交通标识牌字符提取算法

为解决智能交通系统中道路标志牌字符提取问题,提出了一种快速的基于颜色与笔画的新算法。首先采用主元分析方法提取标志牌颜色特征并进行定位,然后对确认后的标志牌区域进行仿射处理,获得容易进行文字提取的图像。最后根据形态学的top hat、skeleton算子以及区域生长等算法得出道路标志牌字符清晰的二值化图像,送OCR软件识别。实验结果显示该算法具有很强的准确性和鲁棒性。     

基于彩色图像的指示标志检测

智能交通系统是近年来国内外广泛关注的研究课题,在基于计算机视觉系统的交通标志识别中,关键步骤之一是如何快速有效地检测并提取交通标志。该文提出了基于色彩的指示标志检测与提取的快速有效方法。它包括：对实景图像进行色调不变的彩色增强,彩色聚类,形态滤波,模板匹配,特征点判别等步骤。最后,由搜索到的特征点从增强后的彩色图像中抽取指示标志。实验显示该方法能快速有效地从实景图像中检测指示标志。     

淮河流域防洪防污智能调度系统的设计

淮河流域是我国洪涝灾害发生最为频繁、历史上多灾多难的地区之一。近年来,随着淮河沿岸工农业生产的发展和人口密度的急剧增加,该流域的水污染问题已发展到直接危及沿淮两岸人民生存的地步。在过去的淮河治理中,对于如何充分有效地综合利用水利工程项目研究得很少,缺乏较大规模的综合性的联合优化调度系统,没有能够充分发挥水利工程的防洪防污作用。为此,本系统针对淮河流域讨论将气象预报、洪水预报、防洪调度和防污调度集成为一体的智能调度系统,其设计方案可为其它流域的治理提供借鉴经验。     

自然场景中道路交通标志形状的检测与校正

道路交通标志的自动识别可以应用于车辆的自动或半自动辅助驾驶系统中,为车辆驾驶提供对周围环境的理解。道路情况的复杂性使得交通标志的背景很复杂,加之光照条件的变化以及车辆运行过程中的抖动等,这些都会导致交通标志失真及变形。本文提出一种快速有效的交通标志形状检测及校正算法,先结合颜色和形状信息,有效定位道路标志位置,然后再利用透视变换校正道路标志。实验结果表明,该方法对光照具有很好的鲁棒性,而且能很好地处理相机拍摄角度引起道路标志在图像中的形变问题。     

Human factors and road safety. Overview of research at the University of Michigan Transportation Research Institute between 1977 and 1986

This article presents a brief overview of the research performed at tge Human Factors Division of The University of Michigan Transportation Research Institute between 1977 and 1986. The focus of the research has been on human factors (ergonomics) aspects of road safety. Specifically, the research has dealt with the following issues: vehicle headlighting, vehicle rear lighting and signalling, vehicle displays and controls, vehicle components, conspicuity of vehicles, legibility of traffic signs and licence plates, driver reaction time, driver performance, stopping sight distance, driver seated position, individual differences (drivers with disabilities, older drivers), methods for measuring blood alcohol concentration, societal violence and traffic accidents, cross-cultural comparison of driver risk-perception, and theoretical issues.     

Screen‐based color vision test for digital quantification of deutan color defects using a color‐calibrated monitor

Display technologies are used in various industries. However, few studies on color vision testing using self‐luminous displays are reported. In this study, a screen‐based color vision test, termed the digital color vision test (DCVT), is developed on a color‐calibrated monitor. A control bar is adjusted by the observer to build just noticeable chromatic difference ellipses in CIE u′v′ chromaticity diagram, which indicate the capabilities of subjects' color visions. Ten color‐normal observers and 13 deutan observers participated in the psychophysical experiments. The observers also performed the other two typical diagnostic tests (D‐15 and anomaloscope). Results from the experiment using the adjustment psychophysical method for digital quantification of deutan color vision defects are presented. It is found that the diagnosis accuracy of the DCVT is approximately equivalent to those of D‐15 and anomaloscope tests.     

改进YOLOv5s的交通标志识别算法

为了准确且实时地检测到交通标志指示牌,减少交通事故的发生和推动智慧交通的发展,针对现有的道路交通标志检测模型存在的精度不足、权重文件大、检测速度慢的问题,设计了一种基于计算机视觉技术的改进YOLOv5s检测算法YOLOv5s-GC.首先,使用copy-paste进行数据增强后再送入网络进行训练,加强对小目标的检测能力;然后,引入Ghost来构建网络,削减原网络的参数和计算量,实现轻量化模型;最后,将坐标注意力机制(coordinate attention)融合到骨干网络里,增强对待测目标的表示和定位能力,提高识别精度.实验结果表明,YOLOv5s-GC模型相比于原YOLOv5s模型,参数数目减少了12%,检测速度提高了22%,平均精度达到了94.2%,易于部署且能满足实际自动驾驶场景中对识别交通标志的速度和准确度要求.     

Flicker Observer Effect: Guiding Attention Through High Frequency Flicker in Images

Drawing the user's gaze to an important item in an image or a graphical user interface is a common challenge. Usually, some form of highlighting is used, such as a clearly distinct color or a border around the item. Flicker can also be very salient, but is often perceived as annoying. In this paper, we explore high frequency flicker (60 to 72 Hz) to guide the user's attention in an image. At such high frequencies, the critical flicker frequency (CFF) threshold is reached, which makes the flicker appear to fuse into a stable signal. However, the CFF is not uniform across the visual field, but is higher in the peripheral vision at normal lighting conditions. Through experiments, we show that high frequency flicker can be easily detected by observers in the peripheral vision, but the signal is hardly visible in the foveal vision when users directly look at the flickering patch. We demonstrate that this property can be used to draw the user's attention to important image regions using a standard high refresh‐rate computer monitor with minimal visible modifications to the image. In an uncalibrated visual search task, users could in a crowded image easily spot the specified search targets flickering with very high frequency. They also reported that high frequency flicker was distracting when they had to attend to another region, while it was hardly noticeable when looking at the flickering region itself.     

道路交通标志检测分类方法的研究

研究了道路交通标志检测分类问题,针对颜色定位检测交通标志的缺陷,提出了一种基于形状边缘定位和颜色判别的交通标志检测分类方法。首先将原图像从RGB色彩空间转换到HSV,在饱和度S通道上用Canny算子检测边缘,计算边缘的形状参数(圆形度、矩形度以及推广得到的正三角形度)以判定边缘形状,定位出标志的位置;然后采用修正的HSV色彩空间分割模型判别标志颜色以进行分类,分类过程中筛除了非标志区域。实验证明,该方法具有良好的检测分类效果。     

Using Electroencephalogram to Investigate Stages of Visual Search in Visually Impaired Computer Users: Preattention and Focal Attention

Researchers have shown that computer users with impaired vision perform visual search more slowly than their fully sighted counterparts. However, little is known about intermediate stages of visual search that exist between stimuli detection and identification. Therefore, the primary focus of this research is to investigate 2 intermediate stages of visual search in visually impaired computer users: preattention and focal attention. A total of 10 volunteers, 1 participant who possessed normal vision and 9 participants possessing impaired vision, underwent clinical visual examinations consisting of assessments of visual acuity, contrast sensitivity, visual field, and color perception. The involvement of each participant's visual cortex while performing a continuous matching task of visual icons on a computer screen was recorded. Results demonstrated that additional time required by visually impaired computer users is not a result of delayed engagement of the visual cortex, but rather due to time spent in active search once the visual cortex has already been engaged. Directions for future research are provided.     

基于智能手机的人行横道红绿灯自动识别

鉴于目前绝大部分人行横道红绿灯没有配合声音提示,利用带摄像头的智能手机,通过人行横道红绿灯的自动识别技术将大大提高盲人过马路的安全系数。提出一个融合级联Adaboost与颜色过滤的人行横道红绿灯自动识别算法。该方法首先利用AdaBoost算法对红绿灯位置进行检测;然后在HSI色彩模型的色调子空间进行颜色分析的基础上进行红绿灯的过滤与分类。用采集的418幅实际复杂场景下的图像数据进行实验,结果表明该方法能达到较好的检测效果。     

Detecting and mapping traffic signs from Google Street View images using deep learning and GIS

Street traffic sign infrastructure remains an extremely difficult asset for local government to manage due to its diverse physical structure and geographical distribution. A spatial registrar of traffic infrastructure is currently a required component of local government councils' mandatory road management plans. Recent advancements of object detection technology in machine learning have presented an automated approach for the detection and classification of street signage captured by Google's Street View (GSV) imagery. This paper explores the possibility of using deep learning to produce an autonomous system for detecting traffic signs on GSV images to assist in traffic assets monitoring and maintenance. By leveraging Google's Street View API, this research offers an economic approach of building purposeful street sign computer vision datasets. A custom object detection model was trained to detect and classify Stop and Give Way signs from images captured at intersection approaches. Considering the output detected bounding box coordinates, photogrammetry approach was applied to calculate the approximate location of each detected sign in two-dimensional geographical space. The newly located and classified street signs can be combined with relevant spatial data for implementation into an asset management system. By combining GIS and the GSV API, the process is completely scalable to any level of street sign classification scope. The experiments conducted on the road network of study area recorded a detection accuracy of 95.63% and classification accuracy of 97.82%. Our proposed automated approach to the detection and localisation of street sign infrastructure has displayed a promising potential for its use by local government authorities. Our workflow can be used to detect other traffic signs and applied to other road sections and other cities. Of primary importance, this approach takes an entirely free and open-source approach throughout. The continuation of Google's Street View program will account for the spatiotemporal representation of street sign infrastructure for the ongoing maintenance and renewal programs of this valuable asset.     

Intelligent guidance for headway and lane control

The principal objective of this study is to define techniques for using graphical imagery alone as guidance information for automotive headway and lane control. The goal is to accelerate, steer, and brake an automobile at normal speeds on both limited-access and conventional highways, including entry/exit ramps, multiple-lane roads, and undivided highways, and accounting for mixed traffic, road signs, traffic signals, and unexpected emergencies. It is expected that a system capable of performing these tasks would consist of one or more television cameras, pattern-recognition logic implemented in digital signal processing chips, a rule-based expert system for guidance and control, conventional motion sensors, and actuators for steering, braking, and throttle control.     

Age and visual impairment decrease driving performance as measured on a closed-road circuit

In this study the effects of visual impairment and age on driving were investigated and related to visual function. Participants were 139 licensed drivers (young, middle-aged, and older participants with normal vision, and older participants with ocular disease). Driving performance was assessed during the daytime on a closed-road driving circuit. Visual performance was assessed using a vision testing battery. Age and visual impairment had a significant detrimental effect on recognition tasks (detection and recognition of signs and hazards), time to complete driving tasks (overall course time, reversing, and maneuvering), maneuvering ability, divided attention, and an overall driving performance index. All vision measures were significantly affected by group membership. A combination of motion sensitivity, useful field of view (UFOV), Pelli-Robson letter contrast sensitivity, and dynamic acuity could predict 50% of the variance in overall driving scores. These results indicate that older drivers with either normal vision or visual impairment had poorer driving performance compared with younger or middle-aged drivers with normal vision. The inclusion of tests such as motion sensitivity and the UFOV significantly improve the predictive power of vision tests for driving performance. Although such measures may not be practical for widespread screening, their application in selected cases should be considered.     

Some factors affecting motorcyclists' conspicuity

Three experiments examined some of the factors that might affect motorcyclists' conspicuity to other road users. In each case, subjects saw a sequence of slides showing traffic, some of which contained a motorcyclist. A record was taken of their reaction times to decide whether or not a motorcyclist was present in each slide. Experiments 1 and 2 examined the effects on conspicuity of headlight use, type of clothing, distance of motorcyclist from viewer, and driving situation (urban or semi-rural). Experiment 3 looked more closely at environmental influences on motorcyclist conspicuity, systematically varying the level of background ‘clutter’ behind the motorcyclist. All three experiments indicate that the effectiveness of the conspicuity aids used, especially clothing, may depend on the situation in which the motorcyclist was located: bright clothing and headlight use may not be infallible aids to conspicuity. Brightness contrast between the motorcyclist and the surroundings may be more important as a determinant of conspicuity than the motorcyclist's brightness per se. Motorcyclists' conspicuity is a more complex issue than has hitherto been acknowledged.     

基于聚类与Hough变换的交通标志检测方法

交通标志检测是进行交通标志识别系统的关键技术,提出一种基于图像的颜色和形状进行交通标志检测的方法.首先对图像进行灰度拉伸和噪声滤出的预处理,然后利用改进的K-means聚类算法对彩色图像进行颜色分割,最后采用基于Hough变换的形状检测技术对交通标志中的特殊形状进行定位,从而实现交通标志的检测.实验结果显示,该方法在各种复杂背景条件下检测出结果的平均正确率达到93.0%,优于同条件的算法且具有较高的实时性.