Depth-enhanced gaze following method

Ji Eun Jeong, Yong Suk Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Gaze following is the task of detecting the point of attention of where a third person gaze is staring in a single image. Existing studies have made some modifications to architectures or have additionally learned the gaze angle, and have achieved notable performances. However, when a complex scene is given, the methods generally predict incorrect locations because of the lack of depth information in an RGB image. In this paper, we propose a novel three-stage deep neural networks algorithm to tackle such challenging scenes using a depth map. We achieve state-of-the-art performance on the GazeFollow dataset and examine possibilities for the research of depth information in image interpretation. Moreover, a qualitative comparison shows that our method works stably and accurately for complex scenes similar to those found in real-world photographs.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
PublisherAssociation for Computing Machinery
Pages1090-1093
Number of pages4
ISBN (Electronic)9781450381048
DOIs
StatePublished - 2021 Mar 22
Event36th Annual ACM Symposium on Applied Computing, SAC 2021 - Virtual, Online, Korea, Republic of
Duration: 2021 Mar 222021 Mar 26

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference36th Annual ACM Symposium on Applied Computing, SAC 2021
Country/TerritoryKorea, Republic of
CityVirtual, Online
Period21/03/2221/03/26

Keywords

  • gaze estimation
  • gaze following
  • salient region detections
  • visual attention

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