ML Anthology

Performance Evaluation of Vision-Based High DOF Human Movement Tracking: A Survey and Human Computer Interaction Perspective

Daniel Heckenberg
CVPRW 2006 pp. 156
doi:10.1109/CVPRW.2006.157 /cvprw/2006/heckenberg2006cvprw-performance/

Abstract

Accurate tracking of human movement has the potential to enable many kinds of human-computer interaction (HCI). Vision-based methods promise tracking without encumberance by body-mounted apparatus but constitute a significant research challenge. Its many applications both in HCI and other areas such as surveillance have made markerless human tracking a central problem in computer vision. Despite this research focus, there are few established mechanisms for evaluating and comparing the performance of reported solutions. Furthermore, very few systems have attempted human-centered evaluation using methods from HCI research. This paper presents a review of evaluation methods employed in the literature of high degree of freedom human movement tracking systems. Detailed full-body tracking and hand tracking systems are included. Suitable evaluation methods employed in other computer vision and HCI research are also considered.

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Cite

Text

Heckenberg. "Performance Evaluation of Vision-Based High DOF Human Movement Tracking: A Survey and Human Computer Interaction Perspective." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006. doi:10.1109/CVPRW.2006.157

Markdown

[Heckenberg. "Performance Evaluation of Vision-Based High DOF Human Movement Tracking: A Survey and Human Computer Interaction Perspective." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2006.](https://mlanthology.org/cvprw/2006/heckenberg2006cvprw-performance/) doi:10.1109/CVPRW.2006.157

BibTeX

@inproceedings{heckenberg2006cvprw-performance,
  title     = {{Performance Evaluation of Vision-Based High DOF Human Movement Tracking: A Survey and Human Computer Interaction Perspective}},
  author    = {Heckenberg, Daniel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2006},
  pages     = {156},
  doi       = {10.1109/CVPRW.2006.157},
  url       = {https://mlanthology.org/cvprw/2006/heckenberg2006cvprw-performance/}
}