Visual-based Positioning and Pose Estimation
Somnuk Phon-Amnuaisuk, Ken T. Murata, La-Or Kovavisaruch, Tiong-Hoo, Lim, Praphan Pavarangkoon, Takamichi Mizuhara

TL;DR
This paper develops and analyzes two visual-based pipelines for human positioning and pose estimation in video, demonstrating their effectiveness in a badminton game with robust error handling and high-resolution results.
Contribution
It introduces two novel pipelines for visual-based positioning and pose estimation, incorporating tracking and interpolation to improve robustness against errors.
Findings
Effective tracking by detection approach demonstrated
Linear interpolation reduces errors in position and pose
High spatial and temporal resolution achieved
Abstract
Recent advances in deep learning and computer vision offer an excellent opportunity to investigate high-level visual analysis tasks such as human localization and human pose estimation. Although the performance of human localization and human pose estimation has significantly improved in recent reports, they are not perfect and erroneous localization and pose estimation can be expected among video frames. Studies on the integration of these techniques into a generic pipeline that is robust to noise introduced from those errors are still lacking. This paper fills the missing study. We explored and developed two working pipelines that suited the visual-based positioning and pose estimation tasks. Analyses of the proposed pipelines were conducted on a badminton game. We showed that the concept of tracking by detection could work well, and errors in position and pose could be effectively…
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Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Vision and Imaging · Video Surveillance and Tracking Methods
