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Action recognition based on a bag of 3D points
Abstract
This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points from the depth maps. Experimental results have shown that over 90% recognition accuracy were achieved by sampling only about 1% 3D points from the depth maps. Compared to the 2D silhouette based recognition, the recognition errors were halved. In addition, we demonstrate the potential of the bag of points posture model to deal with occlusions through simulation.
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Citations
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
Multivariate LSTM-FCNs for time series classification
Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.04.014 · Cited by 1,138
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
“Hollywood2 [97] √ × × 1707 12 HMDB51 [98] √ × × 6766 51 Olympic Sports [99] √ × × 783 16 UCF50 [100] √ × × 6618 50 UCF101 [101] √ × × 13,320 101 Kinetics [102] √ × × 306,245 400 MSR-Action3D [103] × √ √ 567 20 MSR-Daily Activity [104] √ √ √ 320 16 Northwestern-UCLA [105] √ √ √ 1475 10 UTD-MHAD [106] √ √ √ 861 27 RGBD-…”
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
RGBD-HuDaAct: A color-depth video database for human daily activity recognition
Ni, B., Wang, G., & Moulin, P. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130379 · Cited by 304
Metadata
- Authors
- Li, W., Zhang, Z., & Liu, Z.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 9-14
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2144380653
- Cited by
- 1,428
- References
- 0
- FWCI
- 34.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Action recognition based on a bag of 3D points. https://app.cochat.ai/papers/10.1109/cvprw.2010.5543273
- The paper
- Li, W., Zhang, Z., & Liu, Z. (2010). Action recognition based on a bag of 3D points. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. https://doi.org/10.1109/cvprw.2010.5543273
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
