Fusion of depth, skeleton, and inertial data for human action recognition

By Cheng Chen, Roozbeh Jafari, Nasser Kehtarnavaz. 2016

66 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. G. Wang
2. Jun Liu
3. Ling-yu Duan
4. Alex ChiChung Kot
5. Ping Hu
6. Amir Shahroudy
7. Junsong Yuan
8. Hossein Rahmani

In Computer Vision and Pattern Recognition

1. Jun Liu
2. Alex ChiChung Kot
3. Ling-yu Duan
4. G. Wang
5. Ping Hu
6. Amir Shahroudy
7. Hossein Rahmani
8. Qiuhong Ke

In Human-Computer Interaction

1. Jun Liu
2. Alex ChiChung Kot
3. Ling-yu Duan
4. Ping Hu
5. G. Wang

Citations in the corpus

• Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks (2017, neutral): “nition have been proposed [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41].”
• Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, neutral): “Skeleton-based action recognition has been explored in different aspects in recent years [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57].”
• Global Context-Aware Attention LSTM Networks for 3D Action Recognition (2017, background): “Various feature extractors and classifier learning methods for 3D action recognition have been proposed in the past few years [28, 37, 31, 65, 54, 26, 34, 5, 47, 59, 38, 56, 32, 2].”