  Object detection is a mature problem in autonomous driving with pedestrian
detection being one of the first deployed algorithms. It has been
comprehensively studied in the literature. However, object detection is
relatively less explored for fisheye cameras used for surround-view near field
sensing. The standard bounding box representation fails in fisheye cameras due
to heavy radial distortion, particularly in the periphery. To mitigate this, we
explore extending the standard object detection output representation of
bounding box. We design rotated bounding boxes, ellipse, generic polygon as
polar arc/angle representations and define an instance segmentation mIOU metric
to analyze these representations. The proposed model FisheyeDetNet with polygon
outperforms others and achieves a mAP score of 49.5 % on Valeo fisheye
surround-view dataset for automated driving applications. This dataset has 60K
images captured from 4 surround-view cameras across Europe, North America and
Asia. To the best of our knowledge, this is the first detailed study on object
detection on fisheye cameras for autonomous driving scenarios.
