Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
This work addresses the need for reliable traffic sign detection in autonomous driving by focusing on challenging conditions, but it is incremental as it builds on existing datasets and competition frameworks.
The paper tackled the problem of robust traffic sign detection under challenging conditions for autonomous vehicles by introducing the CURE-TSD video dataset and summarizing the VIP Cup 2017 competition, which involved multiple teams implementing detection algorithms.
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.