Generalized Object Detection on Fisheye Cameras for Autonomous Driving:
Dataset, Representations and Baseline
- URL: http://arxiv.org/abs/2012.02124v1
- Date: Thu, 3 Dec 2020 18:00:16 GMT
- Title: Generalized Object Detection on Fisheye Cameras for Autonomous Driving:
Dataset, Representations and Baseline
- Authors: Hazem Rashed, Eslam Mohamed, Ganesh Sistu, Varun Ravi Kumar, Ciaran
Eising, Ahmad El-Sallab and Senthil Yogamani
- Abstract summary: We explore better representations like oriented bounding box, ellipse, and generic polygon for object detection in fisheye images.
We design a novel curved bounding box model that has optimal properties for fisheye distortion models.
It is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios.
- Score: 5.1450366450434295
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Object detection is a comprehensively studied problem in autonomous driving.
However, it has been relatively less explored in the case of fisheye cameras.
The standard bounding box fails in fisheye cameras due to the strong radial
distortion, particularly in the image's periphery. We explore better
representations like oriented bounding box, ellipse, and generic polygon for
object detection in fisheye images in this work. We use the IoU metric to
compare these representations using accurate instance segmentation ground
truth. We design a novel curved bounding box model that has optimal properties
for fisheye distortion models. We also design a curvature adaptive perimeter
sampling method for obtaining polygon vertices, improving relative mAP score by
4.9% compared to uniform sampling. Overall, the proposed polygon model improves
mIoU relative accuracy by 40.3%. It is the first detailed study on object
detection on fisheye cameras for autonomous driving scenarios to the best of
our knowledge. The dataset comprising of 10,000 images along with all the
object representations ground truth will be made public to encourage further
research. We summarize our work in a short video with qualitative results at
https://youtu.be/iLkOzvJpL-A.
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