Human-object maps for daily activity recognition

Haruya Ishikawa, Yuchi Ishikawa, Shuichi Akizuki, Yoshimitsu Aoki

研究成果: Conference contribution

抄録

In the field of action recognition, when and where an interaction between a human and an object happens has the potential to be valid information in enhancing action recognition accuracy. Especially, in daily life where each activities are performed in longer time frame, conventional short term action recognition may fail to generalize do to the variety of shorter actions that could take place during the activity. In this paper, we propose a novel representation of human object interaction called Human-Object Maps (HOMs) for recognition of long term daily activities. HOMs are 2D probability maps that represents spatio-temporal information of human object interaction in a given scene. We analyzed the effectiveness of HOMs as well as features relating to the time of the day in daily activity recognition. Since there are no publicly available daily activity dataset that depicts daily routines needed for our task, we have created a new dataset that contains long term activities. Using this dataset, we confirm that our method enhances the prediction accuracy of the conventional 3D ResNeXt action recognition method from 86.31% to 97.89%.

本文言語English
ホスト出版物のタイトルProceedings of the 16th International Conference on Machine Vision Applications, MVA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9784901122184
DOI
出版ステータスPublished - 2019 5月
イベント16th International Conference on Machine Vision Applications, MVA 2019 - Tokyo, Japan
継続期間: 2019 5月 272019 5月 31

出版物シリーズ

名前Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019

Conference

Conference16th International Conference on Machine Vision Applications, MVA 2019
国/地域Japan
CityTokyo
Period19/5/2719/5/31

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識

引用スタイル