People Removal Using Edge and Depth Information

Shunsuke Yae, Masaaki Ikehara

研究成果: Conference contribution

1 被引用数 (Scopus)


In this paper, we propose a people removal method from a single image for privacy and other reasons using a three-stage network of depth estimation, semantic segmentation, and inpainting, as shown in Fig. 1. In this three-stage network, we improve semantic segmentation for detecting people. We focus on a special situation of a person and construct a network. It is known that the accuracy of conventional methods can be improved by using edge information. The accuracy of segmentation can be further improved by increasing the accuracy of the edge map. In addition, edge detection does not work well when the person and the background are of the similar color, because edge detects the brightness change of the image. Therefore, in this paper, an adversarial loss function for edge maps is proposed. In addition, since an image with people is expected to have a depth difference from the background image, we use a trained depth estimation network to include the depth image in the input. In this way, it is possible to construct a network for people removal with a high accuracy both quantitatively and qualitatively.

ホスト出版物のタイトル2022 IEEE International Conference on Consumer Electronics, ICCE 2022
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2022
イベント2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States
継続期間: 2022 1月 72022 1月 9


名前Digest of Technical Papers - IEEE International Conference on Consumer Electronics


Conference2022 IEEE International Conference on Consumer Electronics, ICCE 2022
国/地域United States
CityVirtual, Online

ASJC Scopus subject areas

  • 産業および生産工学
  • 電子工学および電気工学


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