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View invariant human action recognition using histograms of 3D joints
Abstract
In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method [6]. The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action 3D dataset and our algorithm outperforms Li et al. [25] on most of the cases.
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Citations
Cited by
A New Representation of Skeleton Sequences for 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.233 · Cited by 691
Towards Environment Independent Device Free Human Activity Recognition
Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., Koutsonikolas, D., et al. (2018) · Proceedings of the 24th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3241539.3241548 · Cited by 573
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2896631 · Cited by 523
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
“[72] used a 3D joint point histogram to represent the human pose, and modeled the action through a discrete hidden Markov model.”
MethodSkeleton 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
Unsupervised Representation Learning With Long-Term Dynamics for Skeleton Based Action Recognition
Zheng, N., Wen, J., Liu, R., Long, L., Dai, J., & Gong, Z. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11853 · Cited by 201
Towards Viewpoint Invariant 3D Human Pose Estimation
Haque, A., Peng, B., Luo, Z., Alahi, A., Yeung, S., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_10 · Cited by 169
Super Normal Vector for Human Activity Recognition with Depth Cameras
Yang, X., & Tian, Y. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2565479 · Cited by 162
A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition
Lin, L., Wang, K., Zuo, W., Wang, M., Luo, J., & Zhang, L. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-015-0876-z · Cited by 104
Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
Nie, Q., Liu, Z., & Liu, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58529-7_7 · Cited by 80
Gao, Z., Guo, L., Guan, W., Liu, A.-A., Ren, T., & Chen, S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3038372 · Cited by 63
Fine-grained activity classification in assembly based on multi-visual modalities
Chen, H., Zendehdel, N., Leu, M. C., & Yin, Z. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02152-x · Cited by 47
Li, Q., Lin, W., & Li, J. (2018) · Signal Processing: Image Communication · doi:10.1016/j.image.2018.06.013 · Cited by 19
Lee, D., Soloperto, R., & Saveriano, M. (2018) · Autonomous Robots · doi:10.1007/s10514-017-9645-x · Cited by 11
Deep Learning for Skeleton-Based Human Activity Segmentation: An Autoencoder Approach
Hossen, M. A., Naim, A. G., & Abas, P. E. (2024) · Technologies · doi:10.3390/technologies12070096 · Cited by 7
View Enhanced Jigsaw Puzzle for Self-Supervised Feature Learning in 3D Human Action Recognition
You, W., & Wang, X. (2022) · IEEE Access · doi:10.1109/access.2022.3165040 · Cited by 6
Metadata
- Authors
- Xia, L., Chen, C.-C., & Aggarwal, J. K.
- Venue
- 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 20-27
- ISSN
- 2160-7508 (ISSN-L)
- OpenAlex
- W2145546283
- Cited by
- 1,452
- References
- 0
- FWCI
- 56.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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