Ieee Transactions on Pattern Analysis and Machine Inteligence 1 Super Normal Vector for Human Activity Recognition with Depth Cameras

By Xiaodong Yang, Yingli Tian

144 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Jing Zhang
3. Hong Liu
4. Tae-Kyun Kim
5. Mengyuan Liu
6. Zhiyuan Shi
7. Z. Li
8. Kyungbok Min

Citations in the corpus

• Learning and Refining of Privileged Information-Based RNNs for Action Recognition from Depth Sequences (2017, background): “Action recognition from depth sequences [57, 34, 29, 44, 49] has attracted significant interest recently due to the emergence of low-cost depth sensors.”
• Enhanced skeleton visualization for view invariant human action recognition (2017, unknown): “Depth Hand-crafted HON4D [44] 39.90 SNV [45] 42.80 AOG [39] 53.60 HOPC [40] 80.00 CNN HPM + TM [46] 92.00 Hybrid Hand-crafted AE [47] 76.00 Skeleon Hand-crafted HOJ3D [17] 54.50 LARP [27] 74. method.”
• A Review on Human Activity Recognition Using Vision-Based Method (2017, background): “The approach in [79] is also based on the polynormal which is a cluster of neighboring hypersurface normals from a local spatiotemporal depth volume.”
• LSTM-CNN Architecture for Human Activity Recognition (2020, methodology): “The methods based on fixed sensors mean that the information is obtained from sensors mounted at a fixed position, involving acoustic sensors [7], radars [8], static cameras [9], and other ambient-based sensors.”
• Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “For monitoring, both wearable devices with motion sensors [262] and cameras [263] can be used for human activity recognition.”
• 3D action recognition using data visualization and convolutional neural networks (2017, background): “The depth-based methods, i.e. HON4D [32] and SNV [33] works poorly, due to the various view point changes.”
• Sensor-based and vision-based human activity recognition: A comprehensive survey (2020, methodology): “Vision-based HAR research can be divided based on data type, which includes RGB data [12,107,108] and RGB-D data [9,25] .”
• WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, background): “Camera-based human activity recognition systems can be found in [5], [6].”