LM-Net: a dynamic gesture recognition network with long-term aggregation and motion excitation-Reference-Cited by-同舟云学术

LM-Net: a dynamic gesture recognition network with long-term aggregation and motion excitation

Published:2023-10-13 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Chang Shaopeng,Huang Xueyu

Funder

Key Technologies Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01987-3.pdf

Reference43 articles.

1. Tu Z, Huang Z, Chen Y, Kang D, Bao L, Yang B, Yuan J (2023) Consistent 3d hand reconstruction in video via self-supervised learning. IEEE Trans Pattern Anal Mach Intell 45(8):9469–9485

2. Wei Z, Zeyi L, Jian C, Mingyu K, Xiaoming D, Hongan W (2021) Survey of dynamic hand gesture understanding and interaction. J Softw 32(10):3051–3067

3. Yuanyuan S, Yunan L, Xiaolong F, Kaibin M, Qiguang M (2021) Review of dynamic gesture recognition. Virtual Real Intell Hardw 3(3):183–206

4. Parcheta Z, Martínez-Hinarejos C-D (2017) Sign language gesture recognition using HMM. In: Pattern recognition and image analysis: 8th Iberian conference, IbPRIA 2017, Faro, Portugal, June 20–23, 2017, Proceedings, vol 8. Springer, pp 419–426

5. Carmona J.M, Climent J (2012) A performance evaluation of HMM and DTW for gesture recognition. In: Progress in pattern recognition, image analysis, computer vision, and applications: 17th Iberoamerican congress, CIARP 2012, Buenos Aires, Argentina, September 3–6, 2012. Proceedings 17. Springer, pp 236–243