Joint Distance Maps Based Action Recognition With Convolutional Neural Networks
By Chuankun Li, Yonghong Hou, Pichao Wang, Wanqing Li. 2017
302 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
- Pichao Wang
- Wanqing Li
- Philip O. Ogunbona
- Zhimin Gao
- Chang-Fu Tang
- Jun Liu
- Hossein Rahmani
- Qiuhong Ke
In Computer Vision and Pattern Recognition
- Pichao Wang
- Wanqing Li
- Philip O. Ogunbona
- Jun Liu
- Zhimin Gao
- Chang-Fu Tang
- Hossein Rahmani
- Qiuhong Ke
In Human-Computer Interaction
- Pichao Wang
- Philip O. Ogunbona
- Wanqing Li
- Zhimin Gao
- Jun Liu
- Chang-Fu Tang
- Yuyao Zhang
- Hossein Rahmani
Citations in the corpus
- Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, methodology): “These methods either extracted features from one modality: depth [49, 60, 29, 59, 24, 51, 52] or RGB [27, 33] or skeleton [43, 55, 56, 4, 34, 19], or fuse the features extracted separately from them at a later stage [28, 16, 10, 58].”
- Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks (2018, unknown): “0% 100% JTM [52] − 73.”
- Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN (2018, unknown): “…CV Deep learning on Lie Group [15] 61.37% 66.95% JTM+CNN [49] 73.40% 75.20% Res-TCN [22] 74.30% 83.10% SkeletonNet(CNN) [20] 75.94% 81.16% JDM+CNN [31] 76.20% 82.30% Clips+CNN+MTLN [21] 79.57% 84.83% Enhanced Visualization+CNN [33] 80.03% 87.21% 1 Layer RNN [46] 56.02% 60.24% 2 Layer RNN [46]…”
- Recognizing Human Actions as the Evolution of Pose Estimation Maps (2018, unknown): “40% Kinect JDM [23] 2017 88.”
- Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “As an extension of these works, the Joint Distance Map (JDM) [171] produces view-invariant color-texture images encoding pair-wise distances of skeleton joints.”