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Machine Vision and Applications
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Machine Vision and Applications - Call for Papers: Special Issue on Human Pose Estimation and Its Applications

Guest Editors

Human pose estimation is an important task in computer vision because it not only benefits other vision tasks like action recognition, person re-identification and virtual try-on but also facilitates applications in real-world domains such as robotics, healthcare, sports, and retail. An effective and efficient human pose estimation system can help robots learn skills from demonstrations, help physical therapists diagnose and rehabilitate patients, help sports analysts or coaches track and train athletes, and help retailers build employee-free stores.

Thanks to the development of deep learning and large-scale datasets, the performance of state-of-the-art human pose estimation approaches has drastically improved in recent years, and they can estimate postures in daily activities and some sports accurately. However, several challenges still exist. For example, (1) it is challenging to estimate postures which rarely or never occur in the training data; (2) it is difficult to handle complex scenarios such as crowded people, motion blur, low-light conditions, and occlusions; (3) it is desirable to establish efficient models which can estimate human poses in real time or on low-power devices; (4) it is exciting to create new applications of human pose estimation that can benefit society or transform industry.

Scope

This special issue on “Human Pose Estimation and Its Applications” aims to provide a significant collection of original algorithms, theories and applications to this field. More precisely, the relevant topics for this special issue include (but are not limited to):

  • Single-person or multi-person human pose estimation
  • 3D human pose estimation (from a single image, multiple views or a video)
  • Human pose tracking
  • Weakly, semi, few-shot, or self-supervised human pose estimation
  • Efficient human pose estimation
  • Fairness, ethics, accountability, and transparency in human pose estimation
  • Human pose estimation beyond normal adults, e.g., infants and people with disabilities
  • Literature reviews/survey
  • Datasets and annotations
  • Applications of human pose estimation in robotics, AR/VR, healthcare, retail, sports, autonomous driving, human-robot interaction/collaboration, etc.

Important Dates

Full paper submission deadline: extended to 1 April 2023
First review notification: June 15, 2023
Revised paper due: August 15, 2023
Final decisions: September 15, 2023

Submission guidelines:

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guidelines available on the MVA website at: https://www.springer.com/138  (this opens in a new tab)

Please select "S.I.: Human Pose Estimation and Its Applications" at the beginning of the submission process.

Author Resources

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.  

All papers will be reviewed following standard reviewing procedures for the Journal. 

Papers must be prepared in accordance with the Journal guidelines: www.springer.com/138 (this opens in a new tab)

Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page (this opens in a new tab), including  FAQs (this opens in a new tab),  Tutorials (this opens in a new tab)  along with Help and Support. (this opens in a new tab)

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