Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives

Chunsheng Liu,Yinhai Wang,Faliang Chang,Shuang Li

doi:10.1109/access.2019.2924947

Chunsheng Liu, Yinhai Wang + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2924947

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Abstract

Traffic signs recognition (TSR) is an important part of some advanced driver-assistance systems (ADASs) and auto driving systems (ADSs). As the first key step of TSR, traffic sign detection (TSD) is a challenging problem because of different types, small sizes, complex driving scenes, and occlusions. In recent years, there have been a large number of TSD algorithms based on machine vision and pattern recognition. In this paper, a comprehensive review of the literature on TSD is presented. We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color- and shape-based methods, machine-learning-based methods, and LIDAR-based methods. The methods in each category are also classified into different subcategories for understanding and summarizing the mechanisms of different methods. For some reviewed methods that lack comparisons on public datasets, we reimplemented part of these methods for comparison. The experimental comparisons and analyses are presented on the reported performance and the performance of our reimplemented methods. Furthermore, future directions and recommendations of the TSD research are given to promote the development of the TSD.

Highlights

Computer vision and pattern recognition based traffic sign detection, tracking and classification methods have been studied for several purposes, such as Advanced Driver Assistance Systems (ADAS) and Auto Driving Systems (ADS)
Traffic sign recognition (TSR) systems consist of two phases of detection and classification; for some traffic sign recognition (TSR) systems, a tracking phase is designed between detection and classification for dealing with video sequences [1]
We review the literature on traffic sign detection (TSD) based on camera or LIDAR, and do comparison and analysis of the reviewed methods based on the reported performance and the performance of our reimplemented methods

Summary

INTRODUCTION

Computer vision and pattern recognition based traffic sign detection, tracking and classification methods have been studied for several purposes, such as Advanced Driver Assistance Systems (ADAS) and Auto Driving Systems (ADS). We review the literature on traffic sign detection (TSD) based on camera or LIDAR, and do comparison and analysis of the reviewed methods based on the reported performance and the performance of our reimplemented methods. For a TSR system, traffic sign detection (TSD) usually is the first key process. C. Liu et al.: Machine Vision-Based Traffic Sign Detection Methods: Review, Analyses, and Perspectives. The binary-tree-based classification method usually classify traffic signs according to the shapes and colors in a coarse-to-fine tree process. Distinguished from these previous surveys, we classify the reviewed methods into fine categories, reimplement part of the TSD methods for comprehensive comparisons of these methods, and review the LIDAR based TSD methods In this survey, we mainly review the TSD methods in last five years, and give analyses and future research suggestions.

TRAFFIC SIGN

COLOR BASED DETECTION METHODS

SHAPE BASED DETECTION METHODS

COLOR AND SHAPE BASED METHODS

Findings

CONCLUSIONS AND PERSPECTIVES