Real-Time Detection and Recognition of Road Traffic Signs

By Jack A. Greenhalgh, Majid Mirmehdi. IEEE transactions on intelligent transportation systems (Print), 2012

395 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/13add3174d09194eef3ab5c1559d66b5ef8b14ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Danijel Skocaj
2. Domen Tabernik
3. Yingli Tian
4. Kun Fu
5. Changshui Zhang
6. Junqi Jin
7. Xuejian Rong
8. Chucai Yi

In Media Technology

1. Danijel Skocaj
2. Domen Tabernik
3. Kun Fu
4. Changshui Zhang
5. Junqi Jin
6. Xuejian Rong
7. Yingli Tian
8. Chucai Yi

Citations in the corpus

• Deep Learning for Large-Scale Traffic-Sign Detection and Recognition (2019, methodology): “approaches with hand-crafted features and machine learning [12], [24], we propose a full feature learning”
• Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks (2014, methodology): “Some popular methods include Bayesian classifiers [2], boosting [3], tree classifiers [4], and support vector machines (SVMs) [5].”
• Recognizing Text-Based Traffic Guide Panels with Cascaded Localization Network (2016, background): “[19] introduced more scene cues like the scene structure to define search regions within each frame, and exhaustedly located a large number of guide sign candidates using MSERs, hue, saturation and value color thresholding.”