scholarly journals Traffic Sign Detection for Intelligent Transportation Systems: A Survey

2021 ◽  
Vol 229 ◽  
pp. 01006
Author(s):  
Ayoub Ellahyani ◽  
Ilyas El Jaafari ◽  
Said Charfi
Keyword(s):  
Intelligent Transportation Systems ◽  
Color Space ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Detection Methods ◽  
Data Sets ◽  
Traffic Sign ◽  
Current State ◽  
Sign Detection ◽  
Traffic Sign Detection

Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.

A Deep Learning Based Traffic Sign Detection for Intelligent Transportation Systems

2021 ◽  
pp. 129-137
Author(s):  
Bao-Long Le ◽  
Gia-Huy Lam ◽  
Xuan-Vinh Nguyen ◽  
The-Manh Nguyen ◽  
Quoc-Loc Duong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Fast Traffic Sign Detection Approach Based on Lightweight Network and Multilayer Proposal Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Hoanh Nguyen
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Feature Map ◽  
Current State ◽  
Detection Approach ◽  
Sign Detection ◽  
Traffic Sign Detection

Vision-based traffic sign detection plays a crucial role in intelligent transportation systems. Recently, many approaches based on deep learning for traffic sign detection have been proposed and showed better performance compared with traditional approaches. However, due to difficult conditions in driving environment and the size of traffic signs in traffic scene images, the performance of deep learning-based methods on small traffic sign detection is still limited. In addition, the inference speed of current state-of-the-art approaches on traffic sign detection is still slow. This paper proposes a deep learning-based approach to improve the performance of small traffic sign detection in driving environments. First, a lightweight and efficient architecture is adopted as the base network to address the issue of the inference speed. To enhance the performance on small traffic sign detection, a deconvolution module is adopted to generate an enhanced feature map by aggregating a lower-level feature map with a higher-level feature map. Then, two improved region proposal networks are used to generate proposals from the highest-level feature map and the enhanced feature map. The proposed improved region proposal network is designed for fast and accuracy proposal generation. In the experiments, the German Traffic Sign Detection Benchmark dataset is used to evaluate the effectiveness of each enhanced module, and the Tsinghua-Tencent 100K dataset is used to compare the effectiveness of the proposed approach with other state-of-the-art approaches on traffic sign detection. Experimental results on Tsinghua-Tencent 100K dataset show that the proposed approach achieves competitive performance compared with current state-of-the-art approaches on traffic sign detection while being faster and simpler.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246 ◽  
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

Optimizing Aggregation Level for Intelligent Transportation System Data Based on Wavelet Decomposition

2003 ◽  
Vol 1840 (1) ◽  
pp. 10-20 ◽  
Author(s):  
Fengxiang Qiao ◽  
Xin Wang ◽  
Lei Yu
Keyword(s):  
Intelligent Transportation Systems ◽  
Wavelet Decomposition ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Data Sets ◽  
Time Data ◽  
Aggregated Data ◽  
A New Technique ◽  
Aggregation Level

Appropriate aggregation levels and sampling frames of real-time data in intelligent transportation systems (ITSs) are indispensable to transportation planners and engineers. Conventional techniques for the retrieval of aggregation levels are normally based on statistical comparison of the original and the aggregated data sets. However, it is not guaranteed that errors and noise will not be transmitted to the aggregated data sets and that the desired information will be reserved. Wavelet decomposition is a new technique that can be applied to the determination of aggregation level. An optimization process that can provide the optimized aggregation level of ITS data for different applications was developed. To illustrate the proposed technique, ITS data archived by the TransGuide Center in San Antonio, Texas, were used for a case study. Aggregation levels for different days of a week and different time periods over the whole year of 2001 were obtained through the proposed approach.

scholarly journals End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

2021 ◽  
Author(s):  
Qing Xu ◽  
Xuewu Lin ◽  
Mengchi CAI ◽  
Yu-ang Guo ◽  
Chuang Zhang ◽  
...  
Keyword(s):  
Object Detection ◽  
Intelligent Transportation Systems ◽  
Data Association ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Detection Methods ◽  
Absolute Size ◽  
Detection And Tracking ◽  
Global Correlation ◽  
End To End

Abstract Environment perception is one of the most critical technology of intelligent transportation systems (ITS). Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking (MOT). However, most existing MOT algorithms follow the tracking-by-detection framework, which separates detection and tracking into two independent segments and limit the global efficiency. Recently, a few algorithms have combined feature extraction into one network; however, the tracking portion continues to rely on data association, and requires complex post-processing for life cycle management. Those methods do not combine detection and tracking efficiently. This paper presents a novel network to realize joint multiobject detection and tracking in an end-to-end manner for ITS, named as global correlation network (GCNet). Unlike most object detection methods, GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes, instead of offsetting predictions. The pipeline of detection and tracking in GCNet is conceptually simple, and does not require complicated tracking strategies such as non-maximum suppression and data association. GCNet was evaluated on a multi-vehicle tracking dataset, UA-DETRAC, demonstrating promising performance compared to state-of-the-art detectors and trackers.

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86578-86596 ◽  
Author(s):  
Chunsheng Liu ◽  
Shuang Li ◽  
Faliang Chang ◽  
Yinhai Wang
Keyword(s):  
Machine Vision ◽  
Detection Methods ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Supplemental Boosting and Cascaded ConvNet Based Transfer Learning Structure for Fast Traffic Sign Detection in Unknown Application Scenes

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2386 ◽  
Author(s):  
Chunsheng Liu ◽  
Shuang Li ◽  
Faliang Chang ◽  
Wenhui Dong
Keyword(s):  
Transfer Learning ◽  
Intelligent Systems ◽  
High Accuracy ◽  
Transportation Systems ◽  
Detection Problem ◽  
Traffic Sign ◽  
Training Samples ◽  
Sign Detection ◽  
Boosting Method ◽  
Traffic Sign Detection

With rapid calculation speed and relatively high accuracy, the AdaBoost-based detection framework has been successfully applied in some real applications of machine vision-based intelligent systems. The main shortcoming of the AdaBoost-based detection framework is that the off-line trained detector cannot be transfer retrained to adapt to unknown application scenes. In this paper, a new transfer learning structure based on two novel methods of supplemental boosting and cascaded ConvNet is proposed to address this shortcoming. The supplemental boosting method is proposed to supplementally retrain an AdaBoost-based detector for the purpose of transferring a detector to adapt to unknown application scenes. The cascaded ConvNet is designed and attached to the end of the AdaBoost-based detector for improving the detection rate and collecting supplemental training samples. With the added supplemental training samples provided by the cascaded ConvNet, the AdaBoost-based detector can be retrained with the supplemental boosting method. The detector combined with the retrained boosted detector and cascaded ConvNet detector can achieve high accuracy and a short detection time. As a representative object detection problem in intelligent transportation systems, the traffic sign detection problem is chosen to show our method. Through experiments with the public datasets from different countries, we show that the proposed framework can quickly detect objects in unknown application scenes.

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