Online RGB-D gesture recognition with extreme learning machines

By Xi Chen, Markus Koskela. 2013

47 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. Fei Han
2. Christopher M. Reardon
3. William A. Hoff
4. Brian Reily
5. Xue Yang
6. Yu Zhang
7. Graham W. Taylor
8. Christian Wolf

In Computer Vision and Pattern Recognition

1. Fei Han
2. William A. Hoff
3. Brian Reily
4. Natalia Neverova
5. Hao Zhang
6. Christian Wolf
7. Christopher M. Reardon
8. Luc Van Gool

In Human-Computer Interaction

1. William A. Hoff
2. Graham W. Taylor
3. Brian Reily
4. Fei Han
5. Christian Wolf
6. Einoshin Suzuki
7. Xue Yang
8. Yu Zhang

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Chen and Koskela [141] implemented a similar feature extraction method based on pairwise relative position of skeleton joints with normalization calculated by ‖pi − pj‖/ ∑ i, j ‖pi − pj‖, i , j, which is illustrated in Fig.”
• Classifying actions based on histogram of oriented velocity vectors (2015, background): “Chen (2013) and Chen and Koskela (2013) proposed an innovative approach for action recognition.”
• Multi-layered gesture recognition with Kinect (2015, methodology): “Chen and Koskela (2013) uses multiple Extreme Learning Machines (ELMs) (Huang et al., 2012) as classifiers for simultaneous components.”
• Multi-scale Deep Learning for Gesture Detection and Localization (2014, methodology): “Similarly, [29] fuse skeletal information with HoG features extracted from the RGB channel, while [30] propose a combination of a covariance descriptor representing skeletal joint data with spatio-temporal interest points extracted from the RGB modality augmented with audio features.”
• TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 ModDrop (2018, unknown): “Similarly, [32], [33], [34] fused skeletal information with HoG features extracted from either RGB or depth, while [35] proposed a combination of a covariance descriptor representing skeletal joint data with spatio-temporal interest points extracted from RGB augmented with audio.”
• Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017, methodology): “Chen and Koskela [12] implemented a feature extraction method based on pairwise relative position of skeletal joints with normalization, and actions were classified by multiple extreme learning machines.”