Action recognition from motion capture data using Meta-Cognitive RBF Network classifier

By Suraj Vantigodi, Venkatesh Babu Radhakrishnan. 2014

33 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. Jun Liu
2. Liang Wang
3. G. Wang
4. Yun Raymond Fu
5. Junsong Yuan
6. Fei Han
7. Hao Zhang
8. Chaoqun Weng

In Human-Computer Interaction

1. Hao Zhang
2. Fei Han
3. Liang Wang
4. William A. Hoff
5. Brian Reily
6. Chaoqun Weng
7. Yong Du
8. Junwu Weng

Citations in the corpus

• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “A large number of skeleton-based human representations using similar histogram encoding methods were also introduced, including Histogram of Joint Position Differences (HJPD) [133], Histogram of Oriented Velocity Vectors (HOVV) [161], and Histogram of Oriented Displacements (HOD) [208], among others”
• Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition (2017, result): “Besides, comparisons with previous works [18, 23, 7, 5, 14] show that our spatio-temporal weights learning method is able to effectively discover key factors of actions and achieve the best performance.”
• Hierarchical recurrent neural network for skeleton based action recognition (2015, unknown): “[29], Kapsouras et al.”
• Skeleton based action recognition with convolutional neural network (2015, result): “In contrast to those handcrafted features based approaches [6, 13, 17, 18], our approach, like [1], is an effective end-to-end solution to skeleton based action recognition.”
• Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold (2016, unknown): “37 - - - RBF Net [35] 97.”