Video Motion Capture from the Part Confidence Maps of Multi-Camera Images by Spatiotemporal Filtering Using the Human Skeletal Model

Takuya Ohashi; Yosuke Ikegami; Kazuki Yamamoto; Wataru Takano; Yoshihiko Nakamura

IROS 2018poster50 citations

Video Motion Capture from the Part Confidence Maps of Multi-Camera Images by Spatiotemporal Filtering Using the Human Skeletal Model

Takuya Ohashi, Yosuke Ikegami, Kazuki Yamamoto, Wataru Takano, Yoshihiko Nakamura

Abstract

This paper discusses video motion capture, namely, 3D reconstruction of human motion from multi-camera images. After the Part Confidence Maps are computed from each camera image, the proposed spatiotemporal filter is applied to deliver the human motion data with accuracy and smoothness for human motion analysis. The spatiotemporal filter uses the human skeleton and mixes temporal smoothing in two-time inverse kinematics computations. The experimental results show that the mean per joint position error was 26.1mm for regular motions and 38.8mm for inverted motions.

BibTeX

@inproceedings{iros2018_videomotioncaptu,
  title = {Video Motion Capture from the Part Confidence Maps of Multi-Camera Images by Spatiotemporal Filtering Using the Human Skeletal Model},
  author = {Takuya Ohashi and Yosuke Ikegami and Kazuki Yamamoto and Wataru Takano and Yoshihiko Nakamura},
  booktitle = {IROS 2018},
  year = {2018}
}