Traffic sign recognition — How far are we from the solution?

By Markus Mathias, Radu Timofte, Rodrigo Benenson, Luc Van Gool. IEEE International Joint Conference on Neural Network, 2013

352 citations recorded by the source corpus.

How researchers see this paper

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

Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking (2014, methodology): “Indeed Mathias et. al., along with other top contenders in the competition, showed very impressive detection results on the GTSDB.”
Detection of traffic signs in real-world images: The German traffic sign detection benchmark (2013, methodology): “As a glimpse to the variety of methods we asked the three best-performing teams to summarize their work for this paper: [33], [34], [38].”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, positive): “In detail, TSDreambooth is a variation of stable diffusion [165] ﬁne-tuned based on the Belgium trafﬁc sign (BelgiumTS) dataset [166].”
Self-Driving Cars: A Survey (2019, background): “, 2013) Benchmarks, with 51,839 and 900 frames, respectively) that learning-based approaches (Houben et al., 2013; Mathias et al., 2013) could finally show their power, although some of them were able to cope with fewer examples (De Souza et al.”
Local Decorrelation For Improved Pedestrian Detection (2014, methodology): “Recent boosted detectors [1, 7] achieve state-of-the-art accuracy on modern benchmarks [10, 22] while retaining computational efficiency.”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, neutral): “KUL [133] 2013 ✓ ✓ ✓ 16k Flanders CC0 1.”