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

Abstract

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.

11 pages, 5 figures

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
• NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
• CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

Cited by in corpus (4)

• Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
• Using Videos to Evaluate Image Model Robustness
• Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
• Distorted Representation Space Characterization Through Backpropagated Gradients