Pedestrian Group Detection with Gesture Features

• HABE Hitoshi

  Faculty of Science Engineering, Kindai University

• HASHIMOTO Tomonori

  Graduate School of Information Science, Osaka University

• MITSUGAMI Ikuhisa

  ISIR, Osaka University

• SUMI Kazuhiko

  College of Science and Engineering, Aoyama Gakuin University

• YAGI Yasushi

  ISIR, Osaka University

Bibliographic Information

Other Title

• 人物のジェスチャーを加味した歩行者グループ検出

Abstract

If groups of visitors in public spaces and commercial facilities can be detected, information depending on the attributes of the groups can be provided, and we can also provide statistics with regards to the usage of the facilities for the owners of the facilities. The features, such as person-to-person distance and gaze direction, are useful for group detection and have been used in a number of works. However, if the scene is crowded or people in a group act separately, the features don’t seem to work well. In this work, we focus on gestures, which indicate the interaction of people, and propose a group detection method using the information of gestures. Experimental results using dataset collected in an actual scene demonstrate gesture information improve the accuracy of group detection, especially the recall rate.

Journal

• Journal of Japan Society for Fuzzy Theory and Intelligent Informatics

  Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 29 (3), 605-610, 2017

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• human action analysis
• group action analysis
• range sensor
• gesture
• machine learning
• feature extraction

Details

• CRID: 1390001205187340032
• NII Article ID: 130006893560
• DOI: 10.3156/jsoft.29.3_605
• ISSN: 18817203
• ISSN: 13477986
• Web Site: https://www.jstage.jst.go.jp/article/jsoft/29/3/29_605/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed