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Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN).
“Maxout networks,”
Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C Courville, and Yoshua Bengio, · 2013
Earlier work this paper cites.
“Two-stream convolutional networks for action recognition in videos,”
Karen Simonyan and Andrew Zisserman, · 2014
Earlier work this paper cites.
“Faster R-CNN: Towards real-time object detection with region proposal networks,”
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, · 2015
Earlier work this paper cites.
“NTU RGB+D: A large scale dataset for 3d human activity analysis,”
Amir Shahroudy, Jun Liu, Tian-Tsong Ng, and Gang Wang, · 2016
Earlier work this paper cites.
Wentao Zhu, Cuiling Lan, Junliang Xing, Wenjun Zeng, Yanghao Li, Li Shen, and Xiaohui Xie, · 2016
Cited alongside, same era.
“Online human action detection using joint classification-regression recurrent neural networks,”
Yanghao Li, Cuiling Lan, Junliang Xing, Wenjun Zeng, Chunfeng Yuan, and Jiaying Liu, · 2016
Cited alongside, same era.
“PKU-MMD: A large scale benchmark for continuous multi-modal human action understanding,”
Chunhui Liu, Yueyu Hu, Yanghao Li, Sijie Song, and Jiaying Liu, · 2017
Cited alongside, same era.
“Space-time representation of people based on 3d skeletal data: A review,”
Fei Han, Brian Reily, William Hoff, and Hao Zhang, · 2017
Cited alongside, same era.
“An end-to-end spatio-temporal attention model for human action recognition from skeleton data,”
Sijie Song, Cuiling Lan, Junliang Xing, Wenjun Zeng, and Jiaying Liu, · 2017
Closest in time.
Pengfei Zhang, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jianru Xue, and Nanning Zheng, · 2017
Closest in time.
“A new representation of skeleton sequences for 3d action recognition,”
Qiuhong Ke, Mohammed Bennamoun, Senjian An, Ferdous Sohel, and Farid Boussaid, · 2017
Closest in time.
“R-C3D: Region convolutional 3d network for temporal activity detection,”
Huijuan Xu, Abir Das, and Kate Saenko, · 2017
Closest in time.
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