Detection of U.S. Traffic Signs

Møgelmose, Andreas; Liu, Dongran; Trivedi, Mohan M.

doi:10.1109/tits.2015.2433019

Cited by 84 publications

(42 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other noteworthy works on recent developments in traffic sign recognition are given in [60], [62], [63], [64]. The system of traffic sign recognition can be partitioned into three steps: segmentation, feature selection and detection.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, some other methods were proposed, such as distance to bounding box [85], fast Fourier transform (FFT) of shape signatures [86], tangent functions [71], simple image patches [70], and combinations of various simple features [77]. In [64], integral channel features and aggregate channel features were proposed to detect U.S. traffic signs.…”

Section: B Shape Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

125

View full text Add to dashboard Cite

Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

307

125

View full text Add to dashboard Cite

show abstract

“…Meanwhile, more sophisticated features such as the integral channel features (ICF) [16] or the aggregated channel features (ACF) [17] have also been applied to traffic sign detection systems [18], [19]. The ICF/ACF features have strong discriminative power while being efficiently computed, thus a single classifier shows competitive accuracy compared to the cascade classifiers with simpler features.…”

Section: A Traffic Sign Detectionmentioning

confidence: 99%

Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

Lee¹,

Kim²

2018

IEEE Trans. Intell. Transport. Syst.

132

View full text Add to dashboard Cite

We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise sign boundary. In this work, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2D pose and the shape class of a target traffic sign in an input image, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. The proposed method with architectural optimization provides an accurate traffic sign boundary estimation which is also efficient in compute, showing a detection frame rate higher than 7 frames per second on lowpower mobile platforms.

show abstract

“…The boolean convolutional neural networks (BCNN) in Reference [13] combined the HOG features to detect traffic signs on GTSDB. The integral channel features (ICF) and aggregate channel features (ACF) were applied to USA traffic sign detection separately [14]. The features extraction algorithms are more efficient on linear features than non-linear features.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

Detection of U.S. Traffic Signs

Cited by 84 publications

References 29 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

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

Contact Info

Product

Resources

About