Upper body pose estimation for team sports videos using a poselet-regressor of spine pose and body orientation classifiers conditioned by the spine angle prior

Masaki Hayashi, Kyoko Oshima, Masamoto Tanabiki, Yoshimitsu Aoki

Research output: Contribution to journalArticle

Abstract

We propose a per-frame upper body pose estimation method for sports players captured in low-resolution team sports videos. Using the head-center-aligned upper body region appearance in each frame from the head tracker, our framework estimates (1) 2D spine pose, composed of the head center and the pelvis center locations, and (2) the orientation of the upper body in each frame. Our framework is composed of three steps. In the first step, the head region of the subject player is tracked with a standard tracking-by-detection technique for upper body appearance alignment. In the second step, the relative pelvis center location from the head center is estimated by our newly proposed poselet-regressor in each frame to obtain spine angle priors. In the last step, the body orientation is estimated by the upper body orientation classifier selected by the spine angle range. Owing to the alignment of the body appearance and the usage of multiple body orientation classifiers conditioned by the spine angle prior, our method can robustly estimate the body orientation of a player with a large variation of visual appearances during a game, even during side-poses or self-occluded poses. We tested the performance of our method in both American football and soccer videos.

Original languageEnglish
Pages (from-to)121-137
Number of pages17
JournalIPSJ Transactions on Computer Vision and Applications
Volume7
DOIs
Publication statusPublished - 2015

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Keywords

  • Body orientation estimation
  • Feature selection
  • Human pose estimation
  • Poselets
  • Tracking-by-detection

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

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abstract = "We propose a per-frame upper body pose estimation method for sports players captured in low-resolution team sports videos. Using the head-center-aligned upper body region appearance in each frame from the head tracker, our framework estimates (1) 2D spine pose, composed of the head center and the pelvis center locations, and (2) the orientation of the upper body in each frame. Our framework is composed of three steps. In the first step, the head region of the subject player is tracked with a standard tracking-by-detection technique for upper body appearance alignment. In the second step, the relative pelvis center location from the head center is estimated by our newly proposed poselet-regressor in each frame to obtain spine angle priors. In the last step, the body orientation is estimated by the upper body orientation classifier selected by the spine angle range. Owing to the alignment of the body appearance and the usage of multiple body orientation classifiers conditioned by the spine angle prior, our method can robustly estimate the body orientation of a player with a large variation of visual appearances during a game, even during side-poses or self-occluded poses. We tested the performance of our method in both American football and soccer videos.",
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