The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Super Normal Vector for Activity Recognition Using Depth Sequences
Abstract
This paper presents a new framework for human activity recognition from video sequences captured by a depth camera. We cluster hypersurface normals in a depth sequence to form the polynormal which is used to jointly characterize the local motion and shape information. In order to globally capture the spatial and temporal orders, an adaptive spatio-temporal pyramid is introduced to subdivide a depth video into a set of space-time grids. We then propose a novel scheme of aggregating the low-level polynormals into the super normal vector (SNV) which can be seen as a simplified version of the Fisher kernel representation. In the extensive experiments, we achieve classification results superior to all previous published results on the four public benchmark datasets, i.e., MSRAction3D, MSRDailyActivity3D, MSRGesture3D, and MSRActionPairs3D.
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Citations
Cites
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Cited by
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503
“In an RGBD video, the depth data can be seen as a space-time structure composed of depth information.”
BackgroundMethodInfluentialSkeleton based action recognition with convolutional neural network
Du, Y., Fu, Y., & Wang, L. (2015) · 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486569 · Cited by 445
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Liu, M., & Yuan, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00127 · Cited by 336
Metadata
- Authors
- Yang, X., & Tian, Y.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 804-811
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2091911422
- Cited by
- 388
- References
- 1
- FWCI
- 26.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, X., & Tian, Y. (2014). Super Normal Vector for Activity Recognition Using Depth Sequences. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.108
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
