The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Cross-View Action Modeling, Learning, and Recognition
Open accessAbstract
Existing methods on video-based action recognition are generally view-dependent, i.e., performing recognition from the same views seen in the training data. We present a novel multiview spatio-temporal and-or graph (MST-AOG) representation for cross-view action recognition, i.e., the recognition is performed on the video from an unknown and unseen view. As a compositional model, MST-AOG compactly represents the hierarchical combinatorial structures of cross-view actions by explicitly modeling the geometry, appearance and motion variations. This paper proposes effective methods to learn the structure and parameters of MST-AOG. The inference based on MST-AOG enables action recognition from novel views. The training of MST-AOG takes advantage of the 3D human skeleton data obtained from Kinect cameras to avoid annotating enormous multi-view video frames, which is error-prone and time-consuming, but the recognition does not need 3D information and is based on 2D video input. A new Multiview Action3D dataset has been created and will be released. Extensive experiments have demonstrated that this new action representation significantly improves the accuracy and robustness for cross-view action recognition on 2D videos.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
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
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00132 · Cited by 917
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
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
“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-…”
Metadata
- Authors
- Wang, J., Nie, X., Xia, Y., Wu, Y., & Zhu, S.-C.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 2649-2656
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2054041160
- Cited by
- 659
- References
- 2
- FWCI
- 11.71
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, J., Nie, X., Xia, Y., Wu, Y., & Zhu, S.-C. (2014). Cross-View Action Modeling, Learning, and Recognition. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.339
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