Abstract
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.
Subject
Computer Networks and Communications
Reference52 articles.
1. Su, C., Li, J., Zhang, S., Xing, J., Gao, W., and Tian, Q. (2017, January 29). Pose-driven deep convolutional model for person re-identification. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.
2. Xu, J., Zhao, R., Zhu, F., Wang, H., and Ouyang, W. (2018, January 23). Attention-aware compositional network for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.
3. Thyagarajmurthy, A., Ninad, M.G., Rakesh, B.G., Niranjan, S., and Manvi, B. (2019). Proceedings of the Emerging Research in Electronics, Computer Science and Technology, Springer. Available online: https://link.springer.com/chapter/10.1007/978-981-13-5802-9_66/.
4. Human pose estimation for mitigating false negatives in weapon detection in video-surveillance;Lamas;Neurocomputing,2022
5. An openpose-based child abuse decision system using surveillance video;Yoo;J. Korea Inst. Inf. Commun. Eng.,2019
Cited by
30 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献