View-Invariant 3D Action Recognition Using Spatiotemporal Self-Similarities from Depth Camera

By A.-Reum Lee, Heung-Il Suk, Seong-Whan Lee. 2014

9 citations recorded by the source corpus.

Subfields: Biomedical Engineering, 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 Biomedical Engineering

1. Hong Liu
2. Qinqin He
3. Mengyuan Liu
4. Hanqing Lu
5. Yifan Zhang
6. Jian Cheng
7. Lei Shi
8. Liang Wang

In Computer Vision and Pattern Recognition

1. Hong Liu
2. Qinqin He
3. Mengyuan Liu
4. Hanqing Lu
5. Jian Cheng
6. Yifan Zhang
7. Lei Shi
8. Liang Wang

Citations in the corpus

• RGB-D-based action recognition datasets: A survey (2016, neutral): “irst 7 training, last 5 test) 2. CS (First 7 training, last 5 test) 3. 5-fold group-wise CV 4. LOSubO 5. LOSubO ATC42 [40] 1. Depth-VSFR(All-view) [67] 2. Depth-VSFR(Cross-view) [67] 3. SSM(All-view) [98] 4. SSM(Cross-view) [98] 1. 85.5 2. 82.0 3. 83.4 4. 81.2 1. D 2. D 3. D 4. D 1. LOSubO CV 2. Cro”
• Fusing shape and motion matrices for view invariant action recognition using 3D skeletons (2017, background): “Based on depth and skeleton data, many works [8, 9, 10, 11, 12, 13] have been done to tackle with viewpoint variations.”