Driver Identification of Landmarks and Traffic Signs after a Stroke

2005 ◽  
Vol 1922 (1) ◽  
pp. 9-14
Author(s):  
Ergun Y. Uc ◽  
Matthew Rizzo ◽  
Steven W. Anderson ◽  
Qian Shi ◽  
Jeffrey D. Dawson
Keyword(s):  
Visual Search ◽  
Target Identification ◽  
Driving Safety ◽  
Cognitive Resources ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Neurologically Normal ◽  
Driver Identification ◽  
Search And Recognition ◽  
Visual Cognitive

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.

scholarly journals MENGENALI RAMBU LALU LINTAS MENGGUNAKAN METODE HOG DAN KNN

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Agnes Dirgahayu Palit
Keyword(s):  
Road Safety ◽  
Performance Testing ◽  
Driving Safety ◽  
Traffic Sign Recognition ◽  
K Nearest Neighbor ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Sign Recognition ◽  
On The Road

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk membantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Recognition (Sistem Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan metode Histogram of Oriented Gradient (HOG), untuk ektraksi ciri citra, dan K-Nearest Neighbour (KNN) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mengenali citra yang merupakan objek rambu lalu lintas Serta bagaimana performansi akurasi sistem. Diharapkan dengan adanya paduan metode-metode tersebut dapat dilihat bagaimana sistem merekognisi rambu lalu lintas. Dari hasil pengujian performansi sistem dengan nilai k=3, diperoleh akurasi sistem 79.4444 %.Kata kunci : ekstraksi ciri, klasifikasi, HOG, KNN.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs designed to help drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Recognition. The system uses a method Histogram of Oriented Gradient (HOG), for the feature extraction of image characteristics, and K-Nearest Neighbor (KNN) to classify traffic signs image. So, it can be analyzed how the system can detect and recognize the image which is the object of traffic signs And how the accuracy of the system performance. Expected by the blend of these methods can be seen how the system detects and merekognisi traffic signs. From the results of performance testing system with a value of k = 3, acquired 79.4444% system accuracy.

scholarly journals Study on the Deocclusion of the Visibility Window of Traffic Signs on a Curved Highway

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jian Xiao ◽  
Jian Zhao ◽  
Liulin Yang ◽  
Juanxia He ◽  
Yu Li ◽  
...  
Keyword(s):  
Crucial Role ◽  
Visual Recognition ◽  
Driving Safety ◽  
Complex Topography ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Design Formula ◽  
Horizontal Alignment ◽  
Visual Occlusion

Highway navigation is often affected by complex topography, and the flat curve plays an important role in the horizontal alignment design of a highway. Many curves are formed, where visibility could be decreased. Thus, the indicative function of a traffic sign plays a crucial role in ensuring driving safety at the curve. Due to the blocked visibility, the probability of the traffic sign occlusion at the curve of operating highways is quite high. It is urgent to consider the clearing obstructions around traffic signs at curves during highway construction. In this study, the potential of visual occlusion for traffic signs on curved highways was investigated. Firstly, the driver’s visibility window that contains traffic signs was defined and criteria of visual occlusion were proposed. Secondly, a geometric occlusion design formula was established to mimic the visual recognition process of traffic signs on a curved highway, yielding the formula to calculate the visibility window. Finally, the occlusion design formula was applied into a case study of the Beijing-Hong Kong-Macau Expressway (Hunan section), in which visibility windows were calculated and analyzed. The obtained results verified the correctness and effectiveness of the occlusion design formula developed in this study.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

scholarly journals Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning

2021 ◽  
pp. 215-219
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Classification Model ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Electric Cars ◽  
Blob Detection ◽  
Sign Recognition ◽  
Testing Dataset ◽  
Self Driving Cars ◽  
Traffic Sign Classification

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

scholarly journals Automatic mapping of traffic signs

2021 ◽  
Vol 4 ◽  
pp. 1-6
Author(s):  
Bence Dusek ◽  
Mátyás Gede
Keyword(s):  
Inertial Measurement Unit ◽  
Local Coordinate System ◽  
Point Of View ◽  
Measurement Unit ◽  
Absolute Position ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Mapping ◽  
The Government ◽  
Cost Efficient

Abstract. Nowadays, people easily can get into their cars and drive hundreds of kilometers in a few hours, but for that to work efficiently a system of rules must be applied and those rules have to be communicated transparently. This is why traffic signs are an influential part of our lives and every kind of information about each is helping the government, the community, and the drivers. This paper presents a novel and cost-efficient method for acquiring information on traffic signs, such like the category and the 3D position. The former can be gained using camera images and a Convolutional Neural Network model. The latter can be obtained using positioning devices.With the help of a GNSS device the absolute position of the vehicle can be learned and based on that a local coordinate system can be established. From the vehicle’s point of view the coordinates and the orientation of the traffic sign can be acquired by applying a stereo camera and an IMU (Inertial Measurement Unit) sensor. Then, with the help of these attributes a large database can be built, maintained, and updated. This project displays that adequately precise data can easily be accessible using a few cheap devices and sensors.

Traffic-Signs Recognition System Based on FCM and Content-Based Image Retrieval

2012 ◽  
pp. 60-72
Author(s):  
Yue Li ◽  
Wei Wang
Keyword(s):  
Image Retrieval ◽  
Recognition System ◽  
Emerging Technology ◽  
Content Based Image Retrieval ◽  
Segmentation Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Intelligent ◽  
Traffic System ◽  
Two Phases

Artificial intelligent (AI) driving is an emerging technology, freeing the driver from driving. Some techniques for automatically driving have been developed; however, most can only recognize the traffic signs in particular groups, such as triangle signs for warning, circle signs for prohibition, and so forth, but cannot tell the exact meaning of every sign. In this paper, a framework for a traffic system recognition system is proposed. This system consists of two phases. The segmentation method, fuzzy c-means (FCM), is used to detect the traffic sign, whereas the Content-Based Image Retrieval (CBIR) method is used to match traffic signs to those in a database to find the exact meaning of every detected sign.

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