ReadingAct RGB-D action dataset and human action recognition from local features
By Lulu Chen, Hong Wei, James M. Ferryman. 2014
19 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Wanqing Li
- Pichao Wang
- Philip O. Ogunbona
- Zhimin Gao
- Chang-Fu Tang
- Jing Zhang
- Yuyao Zhang
- Chengcheng Jia
In Computer Vision and Pattern Recognition
- Wanqing Li
- Pichao Wang
- Philip O. Ogunbona
- Zhimin Gao
- Chang-Fu Tang
- Yuyao Zhang
- Jun Liu
- Yun Raymond Fu
Citations in the corpus
- RGB-D-based action recognition datasets: A survey (2016, methodology): “ReadingAct ReadingAct dataset [44] was collected by Reading University in 2013, using 2 Kinect sensors; one was in front of the subject and the other was placed orthogonally to capture a side view.”
- Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “, Microsoft Kinect ) in 2010, many methods [76, 65, 78, 22, 13, 42, 71, 6, 77, 1, 57, 41, 67, 68, 72, 28, 33, 20, 11, 79, 74, 37, 40, 46, 85, 8, 47] for action recognition have been proposed based on depth, RGB or skeleton data.”
- Low-Rank Tensor Subspace Learning for RGB-D Action Recognition (2016, background): “For low-rank tensor learning, many previous low-rank tensor completion and approximation methods [32]–[35] are widely used for image denoising and alignment recovering.”