Temporal distance matrices for squat classification

Ryoji Ogata, Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

When working out, it is necessary to perform the same action many times for it to have effect. If the action, such as squats or bench pressing, is performed with poor form, it can lead to serious injuries in the long term. For this purpose, we present an action dataset of squats where different types of poor form have been annotated with a diversity of users and backgrounds, and propose a model, based on temporal distance matrices, for the classification task. We first run a 3D pose detector, then we normalize the pose and compute the distance matrix, in which each element represents the distance between two joints. This representation is invariant to differences in individuals, global translation, and global rotation, allowing for high generalization to real world data. Our classification model consists of a CNN with 1D convolutions. Results show that our method significantly outperforms existing approaches for the task.

本文言語English
ホスト出版物のタイトルProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
出版社IEEE Computer Society
ページ2533-2542
ページ数10
ISBN(電子版)9781728125060
DOI
出版ステータスPublished - 2019 6
イベント32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
継続期間: 2019 6 162019 6 20

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
2019-June
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
国/地域United States
CityLong Beach
Period19/6/1619/6/20

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

フィンガープリント

「Temporal distance matrices for squat classification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル