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

Møgelmose, Andreas; Trivedi, Mohan M.; Moeslund, Thomas B.

doi:10.1109/tits.2012.2209421

Cited by 588 publications

(314 citation statements)

References 64 publications

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“…The main shortcoming is that for detection, no standardized dataset is used, so comparison among papers is hard. Only a few public datasets exist that are suitable for detection: The Swedish Traffic Signs Dataset [31], the KUL Belgium Traffic Sign Classification Benchmark [32], and most recently the LISA Dataset [14]. None of them are widely used yet.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Traffic sign detection and analysis: Recent studies and emerging trends

Møgelmose¹,

Trivedi

Moeslund

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-Traffic sign recognition (TSR) is a research field that has seen much activity in the recent decade. This paper introduces the problem and presents 4 recent papers on traffic sign detection and 4 recent papers on traffic sign classification. It attempts to extract recent trends in the field and touch upon unexplored areas, especially the lack of research into integrating TSR with a driver-in-the-loop system and some of the problems that presents. TSR is an exciting field with great promises for integration in driver assistance systems and that particular area deserves to be explored further.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Each of the following subsections cover their methods used for the three blocks: Segmentation, feature extraction, and detection. For further analysis of traffic sign detection methods, see [14]. A.…”

Section: Detectionmentioning

confidence: 99%

Traffic sign detection and analysis: Recent studies and emerging trends

Møgelmose¹,

Trivedi

Moeslund

2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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“…Performing the experiments with a GTA-based synthetic dataset would have resulted in a high number of synthetic samples from the US, but, to the knowledge of the authors, there is no suitable real image dataset of traffic signs from the US available. Most of the street scene images in the afore-mentioned LISA dataset (Mogelmose et al, 2012) have a low quality, e.g. due to compression effects or are blurred.…”

Section: Experiments With the Synthia Datasetmentioning

confidence: 99%

Evaluation of a Traffic Sign Detector by Synthetic Image Data for Advanced Driver Assistance Systems

Hanel

Kreuzpaintner

Stilla

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability for traffic sign detection. The comparison covers the process to produce the synthetic images and addresses the virtual worlds, needed to produce the synthetic images, and their environmental conditions. The comparison covers variations in the appearance of traffic signs and the labeling strategies used for the datasets, as well. A deep learning traffic sign detector is trained with multiple training datasets with different ratios between synthetic and real training samples to evaluate the synthetic SYNTHIA dataset. A test of the detector on real samples only has shown that an overall accuracy and ROC AUC of more than 95% can be achieved for both a small rate of synthetic samples and a large rate of synthetic samples in the training dataset.

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“…Traffic Sign Recognition Systems (TSRs) are important for highway maintenance, driver-assistance systems, and self-driving cars [1]. In general, the research of traffic sign recognition is divided into sign detection and sign classification, which both have been discussed for a long time; in particular, the latter has achieved greater success on the German Traffic Sign Recognition Benchmark (GTSRB).…”

Section: Introductionmentioning

confidence: 99%

“…The details should be taken into account when detection methods are applied to Chinese traffic signs. To evaluate the relative methods for traffic sign detection, there are several public traffic sign datasets available, such as GTSDB [5] and LISA traffic sign dataset (LISATSD) [1]. GTSDB is most commonly used by researchers.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

et al. 2017

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Abstract:Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.

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

Cited by 588 publications

References 64 publications

Traffic sign detection and analysis: Recent studies and emerging trends

Traffic sign detection and analysis: Recent studies and emerging trends

Evaluation of a Traffic Sign Detector by Synthetic Image Data for Advanced Driver Assistance Systems

A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

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