Comparative Analysis of Skeleton-Based Human Pose Estimation

Autor: Jen-Li Chung, Lee-Yeng Ong, Meng-Chew Leow
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Future Internet, Vol 14, Iss 12, p 380 (2022)
Druh dokumentu: article
ISSN: 14120380
1999-5903
DOI: 10.3390/fi14120380
Popis: Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.
Databáze: Directory of Open Access Journals
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