FOE-based regularization for optical flow estimation from an in-vehicle event camera

Jun Nagata, Yusuke Sekikawa, Kosuke Hara, Yoshimitsu Aoki

研究成果: Article査読

1 被引用数 (Scopus)


Optical flow estimation from an in-vehicle camera is an important task in automatic driving and advanced driver-assistance systems. However, there is a problem that optical flow estimation is mistakable with high contrast and high speed. Event camera can overcome these situations because it reports only the per-pixel intensity change with high dynamic range and low latency. However, the L1 smoothness regularization in the conventional optical flow estimation method is not suitable for radial optical flow in the driving scene. Therefore, we propose to use the focus of expansion (FOE) for regularization of optical flow estimation in event camera. The FOE is defined as the intersection of the translation vector of the camera and the image plane. The optical flow becomes radial from the FOE excluding the rotational component. Using the property, the optical flow can be regularized in the correct direction in the optimization process. We demonstrated that the optical flow was improved by introducing our regularization using the public dataset.

ジャーナルElectronics and Communications in Japan
出版ステータスPublished - 2020 4月 1

ASJC Scopus subject areas

  • 信号処理
  • 物理学および天文学(全般)
  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学
  • 応用数学


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