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

TL;DR

This paper discusses the VIP Cup 2017 competition focused on developing traffic sign detection algorithms that are robust under challenging conditions, supported by a new dataset called CURE-TSD.

Contribution

It introduces the CURE-TSD dataset with challenging conditions and provides an overview of the competition setup, approaches, and insights from participants.

Findings

Successful detection under challenging conditions demonstrated by participating teams.

The CURE-TSD dataset enables testing of robust traffic sign detection algorithms.

Insights into effective technical approaches for challenging traffic sign detection.

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.