Traffic Sign Identification Research Articles

Traffic signs identification by using sparse image decomposition and adaptive correlation techniques

Image recognition is an important technology in the present. This paper proposes a new method of image recognising. This approach includes using the morphological component analysis of texture and cartoon layers in image preprocessing, where the matrix of dictionary which is used in the algorithm comes from independent component analysis. Then, use of the adaptive filtering algorithm, which is based on the matching the feature of the standard image and the feature of the original image, highlights the content of the image which is needed to be focused on. In this paper, we select the car camera caught pictures and display the identification effects of the stop sign as an example. Quantitative analysis of the experimental results shows that this proposed method has quite good results and accuracy. At the end of the paper, several recommendations point out its feasible development direction in the future.

Eigen-Gradients for Traffic Sign Recognition

Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

Naturalistic Distraction and Driving Safety in Older Drivers

In this study, we aimed to quantify and compare performance of middle-aged and older drivers during a naturalistic distraction paradigm (visual search for roadside targets) and to predict older drivers performance given functioning in visual, motor, and cognitive domains. Distracted driving can imperil healthy adults and may disproportionally affect the safety of older drivers with visual, motor, and cognitive decline. A total of 203 drivers, 120 healthy older (61 men and 59 women, ages 65 years and older) and 83 middle-aged drivers (38 men and 45 women, ages 40 to 64 years), participated in an on-road test in an instrumented vehicle. Outcome measures included performance in roadside target identification (traffic signs and restaurants) and concurrent driver safety. Differences in visual, motor, and cognitive functioning served as predictors. Older drivers identified fewer landmarks and drove slower but committed more safety errors than did middle-aged drivers. Greater familiarity with local roads benefited performance of middle-aged but not older drivers.Visual cognition predicted both traffic sign identification and safety errors, and executive function predicted traffic sign identification over and above vision. Older adults are susceptible to driving safety errors while distracted by common secondary visual search tasks that are inherent to driving. The findings underscore that age-related cognitive decline affects older drivers' management of driving tasks at multiple levels and can help inform the design of on-road tests and interventions for older drivers.

基于不变性联想神经网络的道路交通标志识别算法

基于不变性联想神经网络的道路交通标志识别算法

Sign location, sign recognition, and driver expectancies

Abstract Background According to police reports, failure to heed signs is one of the most frequent causes of accidents. We examined situations in which experience might paradoxically impair detection and timely identification of traffic signs: when they are located in unexpected places. Hypothesis Experienced drivers have a well-learned schema for scanning the roadway, and will have difficulty detecting traffic signs when their location violates the expectations. Method Twenty experienced drivers were exposed briefly to pictures of real street and road scenes; some included “no right-turn” (NRT) signs in the expected location (on the right curb) and some contained the same sign in an unexpected location (on the left curb). Participants’ eye movements were recorded during the experiment. Results Drivers were less likely to identify the NRT sign when it was located at the unexpected location. Females were less sensitive to sign location and their performance was much better than that of males. Conclusions To increase their timely probability of identification, traffic signs should be posted in expected locations. Schema that drivers bring to the road enables them to handle large amounts of information, but the same schema can endanger drivers if traffic signs placement does not conform to the schema. Implications When signs are misplaced, crash causes can be due to inappropriate design, rather than inappropriate driving. Highway designers should ensure that their design conforms to standards that shape drivers’ expectations.

The Relation Between Driving Experience and Recognition of Road Signs Relative to Their Locations

Examine how driving experience and expectations affect the ability of experienced drivers to identify traffic signs--specifically, no right turn (NRT) and no left turn (NLT) at intersections. Failure to heed signs is a frequent cause of accidents, and the authors focused on the contributions of experience and expectancy to sign identification. Inexperienced and experienced drivers were connected to an eye tracker system and briefly exposed to various traffic scenes. Some of the pictures included an NRT sign at the expected location (on the right), and some included the same sign at an unexpected location (on the left). The same procedure was used with an NLT traffic sign. Experienced drivers identified traffic signs better than inexperienced drivers did when the signs were posted at the expected location but identified them worse than did inexperienced drivers when they were at unexpected locations. With experience, drivers' expectations regarding the expected location of traffic signs become so strong that violating these expectancies results in more identification errors among experienced drivers than among inexperienced drivers. To optimize experienced drivers' traffic sign identification, signs must be located in accordance with drivers' expectations--specifically, on the right side of the road. When signs are misplaced, crashes can be caused by inappropriate placement rather than inappropriate driving. Highway designers should ensure that their design conforms to standards that shape experienced drivers' expectations.

Driver landmark and traffic sign identification in early Alzheimer's disease

Driver landmark and traffic sign identification in early Alzheimer's disease

Impaired visual search in drivers with Parkinson's disease

To assess the ability for visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Parkinson's disease (PD). Seventy-nine drivers with PD and 151 neurologically normal older adults underwent a battery of visual, cognitive, and motor tests. The drivers were asked to report sightings of specific landmarks and traffic signs along a four-lane commercial strip during an experimental drive in an instrumented vehicle. The drivers with PD identified significantly fewer landmarks and traffic signs, and they committed more at-fault safety errors during the task than control subjects, even after adjusting for baseline errors. Within the PD group, the most important predictors of landmark and traffic sign identification rate were performances on Useful Field of View (visual speed of processing and attention) and Complex Figure Test-Copy (visuospatial abilities). Trail Making Test (B-A), a measure of cognitive flexibility independent of motor function, was the only independent predictor of at-fault safety errors in drivers with PD. The cognitive and visual deficits associated with PD resulted in impaired visual search while driving, and the increased cognitive load during this task worsened their driving safety.

Driver landmark and traffic sign identification in early Alzheimer’s disease

Objective: To assess visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Alzheimer’s disease. Methods: 33 drivers with...

Driver Identification of Landmarks and Traffic Signs after a Stroke

A study was done to assess the ability for visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with stroke, that is, drivers who have had a stroke. Visual search for roadside targets during automobile driving can compete for a driver's cognitive resources and may impair driving, especially in drivers with cognitive impairment caused by stroke. Thirty-two drivers with stroke and 137 neurologically normal older adults underwent a battery of visual, cognitive, and motor tests and were asked to report sightings of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with stroke identified significantly fewer landmarks and traffic signs and showed a tendency to make more at-fault safety errors during the task than did control subjects. Roadside target identification performance and safety errors were predicted by scores on standardized tests of visual, cognitive, and motor function. Drivers with stroke are impaired in a task of visual search and recognition of roadside targets whose demands on visual perception, attention, executive functions, and memory probably increased the cognitive load and worsened their driving safety.

Driver Identification of Landmarks and Traffic Signs After a Stroke

A study was done to assess the ability for visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with stroke, that is, drivers who have had a stroke. Visual search for roadside targets during automobile driving can compete for a driver's cognitive resources and may impair driving, especially in drivers with cognitive impairment caused by stroke. Thirty-two drivers with stroke and 137 neurologically normal older adults underwent a battery of visual, cognitive, and motor tests and were asked to report sightings of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with stroke identified significantly fewer landmarks and traffic signs and showed a tendency to make more at-fault safety errors during the task than did control subjects. Roadside target identification performance and safety errors were predicted by scores on standardized tests of visual, cognitive, and motor function. Drivers w...

Vision Models Based Identification of Traffic Signs

Vision Models Based Identification of Traffic Signs