Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey

O desenvolvimento de sistemas de assistência ao condutor (ADAS, Advanced Driver Assistance Systems) originou uma demanda de técnicas de detecção de placas sinalizadoras em imagens digitais, que estão se tornando cada vez mais robustas. Porém, essas técnicas necessitam de muito recurso computacional para serem executadas em tempo real (30 quadros por segundo). Neste artigo, é apresentado um sistema de detecção de placas sinalizadoras capturadas por câmeras digitais. O modelo proposto consiste de 2 fases de detecção, com o objetivo de juntar técnicas de busca e extração de características, que utilizam o menor custo computacional possível. O modelo possui uma taxa de acurácia acima de 90% na base de dados GTSDB (German Traffic Sign Detection Benchmark) assim como os melhores modelos do estado da arte, porém possui um menor tempo de resposta. Por fim, o sistema foi testado em um ambiente real, por meio de uma câmera digital.

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

Sensors ◽

10.3390/s19092093 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2093 ◽

Cited By ~ 13

Author(s):

Safat B. Wali ◽

Majid A. Abdullah ◽

Mahammad A. Hannan ◽

Aini Hussain ◽

Salina A. Samad ◽

...

Keyword(s):

Performance Metrics ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Traffic Sign ◽

Current Trends ◽

Sign Detection ◽

Comprehensive Survey ◽

Future Vision ◽

Traffic Sign Detection ◽

Detection And Recognition

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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TRAFFIC SIGN DETECTION BASED ON SIMPLE XOR AND DISCRIMINATIVE FEATURES

Jurnal Teknologi ◽

10.11113/jt.v78.8908 ◽

2016 ◽

Vol 78 (6-2) ◽

Author(s):

Ahmed Madani ◽

Rubiyah Yusof

Keyword(s):

Shape Matching ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Traffic Sign ◽

Sign Detection ◽

Different Color ◽

Two Stages ◽

Exclusive Or ◽

Testing Speed ◽

Traffic Sign Detection

Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition to detecting Traffic Signs (TSs), the proposed technique also recognizes the shape of the TS. The proposed technique consist of two stages. The first stage is an image segmentation technique that is based on Learning Vector Quantization (LVQ), which divides the image into six different color regions. The second stage is based on discriminative features (area, color, and aspect ratio) and the exclusive OR logical operator (XOR). The output is the location and shape of the TS. The proposed technique is applied on the German Traffic Sign Detection Benchmark (GTSDB), and achieves overall detection and shape matching of around 97% and 100% respectively. The testing speed is around 0.8 seconds per image on a mainstream PC, and the technique is coded using the Matlab toolbox.

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Traffic Sign Detection for Advanced Driver Assistance System

2018 International Conference On Advances in Communication and Computing Technology (ICACCT) ◽

10.1109/icacct.2018.8529455 ◽

2018 ◽

Cited By ~ 3

Author(s):

Pranjali Susheel Kumar Pandey ◽

Ramesh Kulkarni

Keyword(s):

Driver Assistance System ◽

Assistance System ◽

Advanced Driver Assistance System ◽

Driver Assistance ◽

Traffic Sign ◽

Sign Detection ◽

Traffic Sign Detection

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A collection of easily deployable adversarial traffic sign stickers

at - Automatisierungstechnik ◽

10.1515/auto-2020-0115 ◽

2021 ◽

Vol 69 (6) ◽

pp. 511-523

Author(s):

Henrietta Lengyel ◽

Viktor Remeli ◽

Zsolt Szalay

Keyword(s):

Traffic Safety ◽

Autonomous Driving ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Traffic Sign ◽

Practical Possibility ◽

Special Knowledge ◽

Environment Perception ◽

Assistance Systems ◽

Traffic Sign Classification

Abstract The emergence of new autonomous driving systems and functions – in particular, systems that base their decisions on the output of machine learning subsystems responsible for environment perception – brings a significant change in the risks to the safety and security of transportation. These kinds of Advanced Driver Assistance Systems are vulnerable to new types of malicious attacks, and their properties are often not well understood. This paper demonstrates the theoretical and practical possibility of deliberate physical adversarial attacks against deep learning perception systems in general, with a focus on safety-critical driver assistance applications such as traffic sign classification in particular. Our newly developed traffic sign stickers are different from other similar methods insofar that they require no special knowledge or precision in their creation and deployment, thus they present a realistic and severe threat to traffic safety and security. In this paper we preemptively point out the dangers and easily exploitable weaknesses that current and future systems are bound to face.

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