Open Access

ARTICLE

Skeleton Keypoints Extraction Method Combined with Object Detection

Jiabao Shi¹, Zhao Qiu^1,*, Tao Chen¹, Jiale Lin¹, Hancheng Huang², Yunlong He³, Yu Yang³

1 Hainan University, HaiKou, 570228, China
2 University College London, Gower Street, London WC1E 6BT, UK
3 Hainan Century Network Security Information Technology Co., Ltd., HaiKou, 570000, China

* Corresponding Author: Zhao Qiu. Email:

Journal of New Media 2022, 4(2), 97-106. https://doi.org/10.32604/jnm.2022.027176

Received 12 January 2022; Accepted 20 March 2022; Issue published 13 June 2022

Abstract

Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point extraction method combined with object detection, which can focus on the extraction of skeleton keypoints. After a large number of experiments, our model can be combined with object detection for skeleton points extraction, and the detection efficiency is improved.

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Keywords

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