Fusing Multiple Features for Depth-Based Action Recognition
By Y. Zhu, Wenbin Chen, Guo-qing Guo. 2015
45 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Qiuhong Ke
- G. Wang
- Mohammed Bennamoun
- Amir Shahroudy
- Jun Liu
- Ferdous Sohel
- Farid Boussaïd
- Senjian An
In Computer Vision and Pattern Recognition
- G. Wang
- Qiuhong Ke
- Jun Liu
- Mohammed Bennamoun
- Amir Shahroudy
- Ferdous Sohel
- Farid Boussaïd
- Hossein Rahmani
In Human-Computer Interaction
- G. Wang
- Amir Shahroudy
- Qiuhong Ke
- Yihong Gong
- Mohammed Bennamoun
- Senjian An
- Ferdous Sohel
- Tian-Tsong Ng
Citations in the corpus
- Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, background): “The fusion of various depth based features is also studied by [90].”
- RGB-D-based action recognition datasets: A survey (2016, neutral): “Decision-Level Fusion (Majority Voting) [65] 2.”
- SkeletonNet: Mining Deep Part Features for 3-D Action Recognition (2017, background): “HUMAN action recognition has received increasing attention [1]–[7] due to its wide range of applications such as video surveillance, human-machine interaction and robot control [8].”