End to End Learning Human Pose Detection Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

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End to End Learning Human Pose Detection Using Convolutional Neural Networks

Published:2021 Issue: Volume: Page:135-142
ISSN:2194-5357
Container-title:Machine Learning and Information Processing
language:
Short-container-title:

Author:

Navghare Nilesh D.,Gladence L. Mary

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-33-4859-2_13

Reference20 articles.

1. Z. Zhang et al., Weakly supervised adversarial learning for 3D human pose estimation from point clouds. IEEE Trans. Visual Comput. Graph. 26(5), 1851–1859 (2020)

2. F. Bogo et al., Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. in European Conference on Computer Vision, (Springer, Cham, 2016)

3. T.L. Munea et al., The progress of human pose estimation: a survey and taxonomy of models applied in 2D human pose estimation. IEEE Access 8, 133330–133348 (2020)

4. M. Xing, Z. Feng, Y. Su, J. Zhang, An image cues coding approach for 3D human pose estimation. ACM Trans. Multimedia Comput. Commun. Appl. 15(4), Article 113 (January 2020), 20 pages. https://doi.org/10.1145/3368066

5. N. Navghare et al., Python and OpenCV in automation of live Surveillance. in Machine Learning and Information Processing, (Springer, Singapore, 2020), pp. 235–243

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