Presentation 2021-03-04
Leveraging Human Pose Estimation Model for Sports Video Classification
Soichiro Sato, Masaki Aono,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this paper, we propose a motion classification method of sports videos based on a posture estimation model.Specifically, we introduced features to estimate the coordinates of 17 types of human skeletons, representing the transition of the estimated coordinates, which in turn were generated by PoseNet. In addition to this, we cropped the video frames based on the estimated coordinates of the skeleton were used as input to several DNN models that extended the conventional models. The proposed method for motion classification based on this posture estimation model was applied to the data provided by the MediaEval2020 Sports Video Classification task.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Video Action Recognition / Human Pose Estimation / Deep Learning / 3D CNN
Paper # PRMU2020-76
Date of Issue 2021-02-25 (PRMU)

Conference Information
Committee PRMU / IPSJ-CVIM
Conference Date 2021/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Computer Vision and Pattern Recognition for specific environment
Chair Yoichi Sato(Univ. of Tokyo)
Vice Chair Akisato Kimura(NTT) / Masakazu Iwamura(Osaka Pref. Univ.)
Secretary Akisato Kimura(Mobility Technologies) / Masakazu Iwamura(Chubu Univ.)
Assistant Takashi Shibata(NTT) / Masashi Nishiyama(Tottori Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Leveraging Human Pose Estimation Model for Sports Video Classification
Sub Title (in English)
Keyword(1) Video Action Recognition
Keyword(2) Human Pose Estimation
Keyword(3) Deep Learning
Keyword(4) 3D CNN
1st Author's Name Soichiro Sato
1st Author's Affiliation Toyohashi University of Technology(TUT)
2nd Author's Name Masaki Aono
2nd Author's Affiliation Toyohashi University of Technology(TUT)
Date 2021-03-04
Paper # PRMU2020-76
Volume (vol) vol.120
Number (no) PRMU-409
Page pp.pp.41-46(PRMU),
#Pages 6
Date of Issue 2021-02-25 (PRMU)