Body Surface Context: A New Robust Feature for Action Recognition From Depth Videos

By Yan Song, Jinhui Tang, Fan Liu, Shuicheng Yan. 2014

57 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. G. Wang
2. Dacheng Tao
3. Luc Van Gool
4. Jianfei Cai
5. Jiebo Luo
6. Yi Yang
7. Xiaogang Wang
8. Zicheng Liu

In Human-Computer Interaction

1. G. Wang
2. Jinhui Tang
3. Yan Song
4. Jiebo Luo
5. Siyi Liu
6. Hong Cheng
7. Jianfei Cai
8. Jiwen Lu

Citations in the corpus

• RGB-D-based action recognition datasets: A survey (2016, neutral): “NJUST RGB-D Action NJUST RGB-D Action dataset [50] (http://imag.”
• Effective Active Skeleton Representation for Low Latency Human Action Recognition (2016, methodology): “[6] utilized body surface context to describe the human skeleton structure.”
• Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition (2015, background): “[43] proposed a robust action recognition framework with a new feature: the body surface context.”
• Survey on 3D Hand Gesture Recognition (2016, background): “Considered as a kind of human motion [27] in our daily life, hand gestures have been investigated by human motion analysis [28]–[30], which pay more attention to full-body human poses and activities [31].”
• A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset (2014, unknown): “[36] 2014 - 90.”
• Describing Trajectory of Surface Patch for Human Action Recognition on RGB and Depth Videos (2015, methodology): “As point cloud contains more essential spatial information and can avoid distortion in depth maps, it was adopted directly [20] or to describe body surfaces for action representation [9].”