  Object detection is a comprehensively studied problem in autonomous driving.
However, it has been relatively less explored in the case of fisheye cameras.
The strong radial distortion breaks the translation invariance inductive bias
of Convolutional Neural Networks. Thus, we present the WoodScape fisheye object
detection challenge for autonomous driving which was held as part of the CVPR
2022 Workshop on Omnidirectional Computer Vision (OmniCV). This is one of the
first competitions focused on fisheye camera object detection. We encouraged
the participants to design models which work natively on fisheye images without
rectification. We used CodaLab to host the competition based on the publicly
available WoodScape fisheye dataset. In this paper, we provide a detailed
analysis on the competition which attracted the participation of 120 global
teams and a total of 1492 submissions. We briefly discuss the details of the
winning methods and analyze their qualitative and quantitative results.
