# Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]

arXiv: https://arxiv.org/abs/1810.06169 · HTML: https://mlsanity.com/paper/1810.06169/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 4 of 4)

- [Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance](https://mlsanity.com/paper/2208.08664/) (2022)
- [Do Perceptually Aligned Gradients Imply Adversarial Robustness?](https://mlsanity.com/paper/2207.11378/) (2022)
- [Class-Conditioned Transformation for Enhanced Robust Image Classification](https://mlsanity.com/paper/2303.15409/) (2023)
- [TestifAI: Tomography-Based Testing for Deep Learning Systems](https://mlsanity.com/paper/2608.18900/) (2026)

## Abstract (arXiv)

Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe challenging conditions. VIP Cup 2017 competition focused on detecting such traffic signs under challenging conditions. To facilitate such task and competition, we introduced a video dataset denoted as CURE-TSD that includes a variety of challenging conditions. The goal of this challenge was to implement traffic sign detection algorithms that can robustly perform under such challenging conditions. In this article, we share an overview of the VIP Cup 2017 experience including competition setup, teams, technical approaches, participation statistics, and competition experience through finalist teams members' and organizers' eyes.