Visual-based Positioning and Pose Estimation

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 handled by a linear interpolation technique using information from nearby frames. The results showed that the Visual-based Positioning and Pose Estimation could deliver position and pose estimations with good spatial and temporal resolutions.