At Valeo, we develop wide field of view fisheye cameras and computer vision software in order to enable automated driving and parking. A common vehicle sensor configuration can consist of multiple fisheye cameras placed around the vehicle. This allows 360° perception which is key for achieving autonomous driving. Computer vision consists of multiple perception tasks that process the images coming from these cameras in real time to provide pedestrian and vehicle detection, lane marking detection, curb detection, and so on. The development of many of these perception tasks rely on training neural networks (Deep Learning) using many tens of thousands of human annotated examples in order to learn how to identify such objects in the image. Generating massive amounts of labelled data is complex, time consuming and expensive, but is a necessity in order to meet the required perception detection rates and accuracy levels to achieve vehicle autonomy. While there exists many publicly available automotive datasets of single narrow field of view cameras, until now there are no surround view fisheye datasets available to the public that would enable state of the art research, both industrial and academic.

The Challenge

The objective of this challenge is to advance the state of the art and to benchmark techniques for object detection on fisheye images. The challenge is hosted on CodaLab.

The Prize

• 1,000 EUR reward through sponsorship from Lero, to the team/individual at the top of the leaderboard.

• The winning individual/team is expected to present their technical solution in a speaking slot at the OmniCV workshop event. There is no associated paper or poster required.

• The prize will be awarded through a single payment to the Team Lead. Distribution of the prize amongst team members is the responsibility of the Team Lead.

• In the case of a tie the prize will be split.

• The final award remains at the discretion of the organizing committee.

• The final leaderboard will be published on the competition website. We encourage participants to share details of their solutions by sharing links to associated publications and code.

Challenge Rules

• Individuals and teams (unlimited size) can enter the competition.

• Limit of 10 submissions per day and 100 submissions in total per person/team.

• External data, freely & publicly available, is allowed. This includes pre-trained models.

• There are no limits on training time or network capacity.

• No Valeo employees may take part in the challenge.

• Employees of third party companies, universities or institutions that contributed to the creation or have access to the full WoodScape dataset may not take part in the challenge.

• The associated WoodScape dataset terms of use continue to apply for the data usage within this challenge.

Timeline

• Competition Release - April 15, 2022

• Dev Phase Deadline - June 3, 2022

• Test Phase Deadline - June 5, 2022

• Winner informed - June 6, 2022

• OmniCV 2022 winner presentation - June 20, 2022

• All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.

Sponsors

Forum & Contact

We have setup a forum for public queries and discussion in CodaLab, and for private queries, please send an email to saravanabalagi [at] gmail [dot] com