魚眼カメラ向け一般物体検出：データセット・表現・ベースライン
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline
魚眼カメラの歪みに対応するため、曲線バウンディングボックスや楕円・多角形表現を比較検討し、曲率適応型サンプリングで精度を改善した物体検出研究。1万枚のデータセットも公開。
著者: Hazem Rashed, Eslam Mohamed, Ganesh Sistu, Varun Ravi Kumar, Ciaran Eising, Ahmad El-Sallab, Senthil Yogamani
分類: cs.CV, cs.RO
原文アブストラクト
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.
