Traffic sign recognition and analysis for intelligent vehicles
By Arturo de la Escalera, Jose M. Armingol, Mario Mata. Image and Vision Computing, 2003
495 citations recorded by the source corpus.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/0b9a758e37d4f7aeb8fa645fff096b0e04138037
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Andreas Møgelmose
- Mohan Manubhai Trivedi
- Jieping Ye
- Yingying Zhu
- Visvanathan Ramesh
- Keyan Chen
- Thomas Baltzer Moeslund
- Zhenwei Shi
In Computer Vision and Pattern Recognition
- Mohan Manubhai Trivedi
- Andreas Møgelmose
- Mingkun Yang
- Thomas Baltzer Moeslund
- Zhenwei Shi
- Jieping Ye
- Minghui Liao
- Keyan Chen
Citations in the corpus
- Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey (2012, background): “Even though this paper is mostly concerned with using TSR for driver assistance, TSR has various well-defined applications nicely summarized here [13].”
- Extensive Tests of Autonomous Driving Technologies (2013, background): “This includes the development of driving assistance systems, such as traffic-sign recognition [16], [17], lane-keeping assistant [18], [19], collision warning [20], adaptive cruise control [21], and fully autonomous driving behaviors, such as GPS waypoint following [22] and vehicle following [23].”
- Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection (2018, methodology): “Color-based methods usually use normalized RGB [19], YUV [20], or HSI [21] color spaces with a designed threshold to extract the region of interest.”
- A system for traffic sign detection, tracking, and recognition using color, shape, and motion information (2005, background): “The majority of recently published sign detection approaches make use of color information [2, 3, 6, 7, 10, 13].”
- Road-Sign Detection and Recognition Based on Support Vector Machines (2007, background): “Finally, conclusions will be presented in Section V.”
- Object Detection in 20 Years: A Survey (2019, methodology): “detection methods are usually based on color thresholding [364–368], visual saliency detection[369], morphological filtering [79], and edge/contour analysis [370, 371].”