BEV-MODNet: 単眼カメラによる自動運転向け鳥瞰図移動物体検出
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving
単眼画像から直接BEV空間で移動物体を検出するエンドツーエンド手法を提案し、12.9k枚のBEVアノテーション付きデータセットを構築した。RGBとオプティカルフローを融合する二ストリーム構成で、画像平面予測の逆射影よりmIoUで13%改善した。
著者: Hazem Rashed, Mariam Essam, Maha Mohamed, Ahmad El Sallab, Senthil Yogamani
分類: cs.CV, cs.RO
原文アブストラクト
Detection of moving objects is a very important task in autonomous driving systems. After the perception phase, motion planning is typically performed in Bird's Eye View (BEV) space. This would require projection of objects detected on the image plane to top view BEV plane. Such a projection is prone to errors due to lack of depth information and noisy mapping in far away areas. CNNs can leverage the global context in the scene to project better. In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input. To the best of our knowledge, such a dataset does not exist and we create an extended KITTI-raw dataset consisting of 12.9k images with annotations of moving object masks in BEV space for five classes. The dataset is intended to be used for class agnostic motion cue based object detection and classes are provided as meta-data for better tuning. We design and implement a two-stream RGB and optical flow fusion architecture which outputs motion segmentation directly in BEV space. We compare it with inverse perspective mapping of state-of-the-art motion segmentation predictions on the image plane. We observe a significant improvement of 13% in mIoU using the simple baseline implementation. This demonstrates the ability to directly learn motion segmentation output in BEV space. Qualitative results of our baseline and the dataset annotations can be found in https://sites.google.com/view/bev-modnet.
