Online action recognition using covariance of shape and motion
By Igor Kviatkovsky, Ehud Rivlin, Ilan Shimshoni. 2014
39 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
- Fei Han
- William A. Hoff
- Brian Reily
- Yu Qiao
- Ioannis A. Kakadiaris
- Thomas Brox
- Christophoros Nikou
- Michalis Vrigkas
In Computer Vision and Pattern Recognition
- Limin Wang
- Yu Qiao
- Luc Van Gool
- Cordelia Schmid
- Jitendra Malik
- Andrew Zisserman
- Xiaogang Wang
- Alan L. Yuille
In Human-Computer Interaction
- Yu Qiao
- Limin Wang
- William A. Hoff
- Brian Reily
- Fei Han
- Ioannis A. Kakadiaris
- Thomas Brox
- Christophoros Nikou
Citations in the corpus
- Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of benchmark datasets with skeleton data, and discuss potential future research directions.”
- ECO: Efficient Convolutional Network for Online Video Understanding (2018, background): “The other works used frame-based hand-crafted features for online action recognition [29,30].”
- A Review of Human Activity Recognition Methods (2015, background): “Kviatkovsky et al. (2014) mixed shape and motion features for online action classification.”
- Incremental learning algorithms and applications (2016, background): “Another emerging area, caused by ubiquitous sensors within smart phones, addresses activity recognition and modeling [69, 1, 89, 74, 99, 68].”
- Real-Time Action Recognition with Enhanced Motion Vector CNNs (2016, background): “Furthermore, Kviatkovsky et al. [16] proposed a covariance descriptor to realize online action recognition.”