Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
By Dogancan Temel, Ghassan Al-Regib. 2018
28 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Quoc V. Le
- Dan Hendrycks
- Percy Liang
- Mohit Bansal
- Yoshua Bengio
- Alan L. Yuille
- Sergey Levine
- Jonathon Shlens
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Quoc V. Le
- Trevor Darrell
- Alan L. Yuille
- Dacheng Tao
- Xiaogang Wang
- Ming-Hsuan Yang
- Jitendra Malik
In Evaluation & Benchmarks
- Quoc V. Le
- Dan Hendrycks
- Jiquan Ngiam
- Brandon Yang
- Thomas G. Dietterich
- Jonathon Shlens
- Ludwig Schmidt
- Balaji Lakshminarayanan
Citations in the corpus
- Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, background): “Temel et al. (2017; 2018); Temel & AlRegib (2018) propose different corrupted datasets for object and traffic sign recognition.”
- Using Videos to Evaluate Image Model Robustness (2019, background): “To better evaluate the extent of model brittleness in the non-adversarial setting, the community has proposed datasets to benchmark robustness (Hendrycks & Dietterich (2018) and Temel & AlRegib (2018)).”