View-Invariant Action Recognition from Point Triplets

By Yuping Shen, Hassan Foroosh. 2009

70 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. Nanning Zheng
2. Pengfei Zhang
3. Jianru Xue
4. Cuiling Lan
5. Wenjun Zeng
6. Junliang Xing
7. Yasushi Makihara
8. Yasushi Yagi

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Yi Yang
4. Xiaogang Wang
5. Dacheng Tao
6. G. Wang
7. Wenjun Zeng
8. Trevor Darrell

Citations in the corpus

• View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017, background): “Researchers have paid much attention to this issue and designed view-invariant approaches for action recognition from color videos [16, 30, 3, 32, 20, 7, 33, 41, 23, 15, 22, 43, 44, 25, 49, 29, 8].”
• Measuring and reducing observational latency when recognizing actions (2011, background): “Defining and measuring the observational latency of a system involves subtle decisions.”
• Latent Multitask Learning for View-Invariant Action Recognition (2013, background): “One way to achieve view invariance could be to reason about a 3D layout of the scene, or 3D volume of the human body, so that the video features can be adapted from one view to another through geometric transformations [29, 28, 23, 18, 14].”
• View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018, background): “Some attempts have been made in previous works to overcome the view variation problem for robust action recognition [3], [8], [9], [14], [15], [19], [24], [26], [31], [36], [37], [39], [40], [50], [52], [53], [60].”
• Inverse Dynamics for Action Recognition (2013, background): “On the other hand, pose-based approaches [9]–[16] usually”
• Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition (2012, background): “Our work is related to general action recognition systems (Ali and Shah 2010; Li et al. 2010; Shen and Foroosh 2009; Yang et al. 2010).”