Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

Lee, Hee-Seok; Kim, Kang

doi:10.1109/tits.2018.2801560

Cited by 127 publications

(67 citation statements)

References 47 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The 2D-3D correspondence search often can be transformed into 2D-2D image based matching, as the 3D models are built from 2D images. Application specific solutions such as building facade segmentation [61], [62] or traffic sign extraction [63] support the matching of high level features (such as windows, doors, walls, tables,. .…”

Section: Evaluation On Real Datasetsmentioning

confidence: 99%

Absolute Pose Estimation of Central Cameras Using Planar Regions

Fröhlich

Tamás

Kató

2021

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.

show abstract

Section: Evaluation On Real Datasetsmentioning

confidence: 99%

Absolute Pose Estimation of Central Cameras Using Planar Regions

Fröhlich

Tamás

Kató

2021

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…Yang et al propose a two-stage method that segments the region of interest (ROI) with color and detects a traffic sign with a CNN-based classifier [ 28 , 29 ]. Lee et al propose a detector whose structure is based on a one-stage CNN detector such as a single shot multi-box detector (SSD) [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization

Choi

Suhr

Jung

2018

Sensors

View full text Add to dashboard Cite

In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.

show abstract

“…Traffic sign detection and recognition are critical parts of an autonomous vehicle (AV) system for its navigational purpose. Currently, traffic sign detection is achieved by deploying state-of-the-art deep learning object detection networks [1]- [4]. For safety reasons, an AV should not miss detecting any sign as such a mistake could result in a catastrophic incident.…”

Section: Introductionmentioning

confidence: 99%

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Rahman

Sünderhauf²,

Dayoub³

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Object detection is an integral part of an autonomous vehicle for its safety-critical and navigational purposes. Traffic signs as objects play a vital role in guiding such systems. However, if the vehicle fails to locate any critical sign, it might make a catastrophic failure. In this paper, we are proposing an approach to identify traffic signs that have been mistakenly discarded by the object detector. The proposed method raises an alarm when it discovers a failure by the object detector to detect a traffic sign. This approach can be useful to evaluate the performance of the detector during the deployment phase. We trained a single shot multi-box object detector to detect traffic signs and used its internal features to train a separate false negative detector (FND). During deployment, FND decides whether the traffic sign detector (TSD) has missed a sign or not. We are using precision and recall to measure the accuracy of FND in two different datasets. For 80% recall, FND has achieved 89.9% precision in Belgium Traffic Sign Detection dataset and 90.8% precision in German Traffic Sign Recognition Benchmark dataset respectively. To the best of our knowledge, our method is the first to tackle this critical aspect of false negative detection in robotic vision. Such a fail-safe mechanism for object detection can improve the engagement of robotic vision systems in our daily life.

show abstract

Simultaneous Traffic Sign Detection and Boundary Estimation Using Convolutional Neural Network

Cited by 127 publications

References 47 publications

Absolute Pose Estimation of Central Cameras Using Planar Regions

Absolute Pose Estimation of Central Cameras Using Planar Regions

FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Contact Info

Product

Resources

About