Pose Adaptive Motion Feature Pooling for Human Action Analysis

By Bingbing Ni, Pierre Moulin, Shuicheng Yan. 2014

24 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

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Yuyao Zhang
7. Yonghong Hou
8. Mengyuan Liu

In Computer Vision and Pattern Recognition

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Yuyao Zhang
7. Yonghong Hou
8. Jun Liu

In Human-Computer Interaction

1. Pichao Wang
2. Philip O. Ogunbona
3. Wanqing Li
4. Zhimin Gao
5. Chang-Fu Tang
6. Yuyao Zhang
7. Jun Liu
8. Yonghong Hou

Citations in the corpus

• RGB-D-based action recognition datasets: A survey (2016, neutral): “. 96.4(Prec.) 84.6(Rec.) 2. 93.8(Prec.) 94.5(Rec.) 3. 93.2(Prec.) 84.6(Rec.) 1. D, S 2. S 3. D 1. LOSubO(1,3,4 training, 2 test) 2. LOSubO 3. LOSubO RGBDHuDaAct [11] 1. BoW-Pyramid [72] 2. PA-Pooling [73] 1. 91.7 2. 85.9 1. C,D 2. C 1. LOSubO 2. LOSubO MSRActionPair [21] 1. BHIM [74] 2. 3D Pose [75]”
• 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].”