Visual-Based Positioning and Pose Estimation

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 performances of human localization and human pose estimation have significantly improved in recent reports, they are not perfect, and erroneous estimation of position and pose can be expected among video frames. Studies on the integration of these techniques into a generic pipeline robust to those errors are still lacking. This paper fills the missing study. We explored and developed two working pipelines that suited 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 linear interpolation of 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.