Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi, Samad; Zabihi, Seyed Mohsen; Beauchemin, Steven S.; Bauer, Michael

doi:10.1109/ivs.2017.7995781

Cited by 15 publications

(12 citation statements)

References 71 publications

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“…For the HOG-SVM method in [34], the utility of only HOG feature is difficult to reduce the effect of noise, and the parameters for kernel function of SVM are difficult to adjust most suitable ones, which cause to poor performance.…”

Section: Overall Results and Discussionmentioning

confidence: 99%

“…The experiments are conducted under normal light condition and weak light condition, and the IE-MSER method of this paper is compared with recent advance methods such as HOG+SVM [34] , FCN (Fully Convolutional Network) [35] , RGB_MSER [36] , YCbCr_DtBs [37] . The results of detection time and recall are shown in Table.1.…”

Section: Experiments and Analysis In Detection Stagementioning

confidence: 99%

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Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Kuang¹,

Fu²,

Liu³

2018

Int. J. Onl. Eng.

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Abstract-Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

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Section: Overall Results and Discussionmentioning

confidence: 99%

Section: Experiments and Analysis In Detection Stagementioning

confidence: 99%

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Kuang¹,

Fu²,

Liu³

2018

Int. J. Onl. Eng.

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show abstract

“…Detection is concerned with locating traffic signs in the input scene images, whereas classification is about determining what type of sign the system is looking at [17,18]. In other words, traffic sign detection involves generating candidate region of interests (ROIs) that are likely to contain regions of traffic signs, while traffic sign classification gets each candidate ROI and tries to identify the exact type of sign or rejects the identified ROI as a false detection [4,19]. Detection and classification usually constitute recognition in the scientific literature.…”

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

129

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The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

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“…e results show that larger and brighter signs can transmit information to the driver more effectively, reduce information collection time, and improve transmission accuracy. Zabihi et al [17] proposed a test method based on the human vision principle to detect and identify traffic signs in the driver's visual field. Huang et al [18] took the driver's factors as a research perspective and analyzed them, which have a significant impact on the driver's understanding of the sign information.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Driver’s Recognition Time of Different Traffic Sign Combinations on Urban Roads via Driving Simulation

Liu

Deng

2021

Journal of Advanced Transportation

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Given the impact of traffic sign combinations (TSC) on the driver’s visual recognition, this paper analyzed the influence on the driver’s visual recognition process. It used the cognitive psychology theory to establish the information transmission model during the traffic sign combinations. It abstracted the information transmission model to construct the driver’s information processing model. Simultaneously, according to the analysis of the traffic sign combinations of the urban roads, this paper carried out the driver’s visual recognition simulation test when the traffic signs were combined, measured the reaction time of the driver’s visual recognition of multiple combinations of traffic signs, and analyzed the driver’s recognition time (DRT) range in the traffic sign combinations. It used correlation analysis, robust estimation, polynomial regression, and other methods to obtain a significant relationship between the driver’s recognition times in different traffic sign combinations (DTSC). Then it built polynomial regression analysis models, fitted the data, and visualized the fitting results. The results show that through the analysis of the experimental data, based on ensuring certain accuracy, the driver’s recognition time of the traffic sign combinations of the urban road increased appropriately. There is a significant relationship between different traffic sign combinations and the driver’s recognition time. As the number of traffic signs increases, the driver’s recognition time increases significantly. Besides, under certain conditions, gender, age, and the driving experience will impact the driver’s recognition time during the traffic sign combinations. The research results can provide the relevant theoretical basis for the setting of urban traffic signs, provide a powerful reference for the revision of various traffic sign setting standards and norms, and provide ideas for future research on the sign system.

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Detection and recognition of traffic signs inside the attentional visual field of drivers

Cited by 15 publications

References 71 publications

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

The Analysis of Driver’s Recognition Time of Different Traffic Sign Combinations on Urban Roads via Driving Simulation

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