Rapid Multiclass Traffic Sign Detection in High-Resolution Images

Chang

2016

J. Electron. Imaging

Self Cite

The multiview appearance of road signs in uncontrolled environments has made the detection of road signs a challenging problem in computer vision. We propose a road sign detection method to detect multiview road signs. This method is based on several algorithms, including the classical cascaded detector, the self-adaptive weighted Gaussian color model (SW-Gaussian model), and a shape context matching method. The classical cascaded detector is used to detect the frontal road signs in video sequences and obtain the parameters for the SW-Gaussian model. The proposed SW-Gaussian model combines the two-dimensional Gaussian model and the normalized red channel together, which can largely enhance the contrast between the red signs and background. The proposed shape context matching method can match shapes with big noise, which is utilized to detect road signs in different directions. The experimental results show that compared with previous detection methods, the proposed multiview detection method can reach higher detection rate in detecting signs with different directions.

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Section: Road Sign Detection Using Cascaded Detectormentioning

confidence: 99%

Multiview road sign detection via self-adaptive color model and shape context matching

Chang

2016

J. Electron. Imaging

Self Cite

“…In essence, all three approaches are rather similar, especially when it comes to features. Another recent paper presenting work on the GTSDB data set is [18], which shows somewhat worse detection performance than the competitors above, but at a faster speed.…”

Section: Related Studiesmentioning

confidence: 92%

Detection of U.S. Traffic Signs

Møgelmose

IEEE Trans. Intell. Transport. Syst.

Trivedi

2015

This paper presents a comprehensive research study of the detection of U.S. traffic signs. Until now, the research in Traffic Sign Recognition systems has been centered on European traffic signs, but signs can look very different across different parts of the world, and a system that works well in Europe may indeed not work in the U.S. We go over the recent advances in traffic sign detection and discuss the differences in signs across the world. Then we present a comprehensive extension to the publicly available LISA-TS traffic sign data set, almost doubling its size, now with high-definition-quality footage. The extension is made with testing of tracking sign detection systems in mind, providing videos of traffic sign passes. We apply the Integral Channel Features and Aggregate Channel Features detection methods to U.S. traffic signs and show performance numbers outperforming all previous research on U.S. signs (while also performing similarly to the state of the art on European signs). Integral Channel Features have previously been used successfully for European signs, whereas Aggregate Channel Features have never been applied to the field of traffic signs. We take a look at the performance differences between the two methods and analyze how they perform on very distinctive signs, as well as white, rectangular signs, which tend to blend into their environment.

IEEE Trans. Intell. Transport. Syst.

“…In [12], saliency features are computed in multiple image scales, and then each feature of different scales is tested in cascade classifiers. In [13] and [14], variants of local binary pattern (LBP) are extracted and fed into cascade classifiers. Region-based features like histogram of gradient (HoG), which compute the statistics of primitive features in the region, are widely used in the final stage of the detection pipeline where an accurate decision on traffic sign/non-traffic sign is required [9], [12], [15].…”

Section: A Traffic Sign Detectionmentioning

confidence: 99%

Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

Lee¹,

Kim²

2018

127

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.