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Actions as Space-Time Shapes
Abstract
Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure and orientation. We show that these features are useful for action recognition, detection and clustering. The method is fast, does not require video alignment and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, non-rigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action, and low quality video.
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Citations
Cited by
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
MoCoGAN: Decomposing Motion and Content for Video Generation
Tulyakov, S., Liu, M.-Y., Yang, X., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00165 · Cited by 1,076
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Decomposing Motion and Content for Natural Video Sequence Prediction
Villegas, R., Yang, J., Hong, S., Lin, X., & Lee, H. (2017) · arXiv · doi:10.48550/arxiv.1706.08033 · Cited by 488
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
Slow feature analysis for human action recognition
Zhang, Z., & Tao, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.157 · Cited by 307
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
A Review on Deep Learning Techniques for Video Prediction
Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J. A., Orts-Escolano, S., Garcia-Rodriguez, J., & Argyros, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045007 · Cited by 301
Notice of Removal: VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
Luo, Z., Chen, D., Zhang, Y., Huang, Y., Wang, L., Shen, Y., Zhao, D., Zhou, J., & Tan, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00984 · Cited by 144
Context-Aware Activity Recognition and Anomaly Detection in Video
Zhu, Y., Nayak, N. M., & Roy-Chowdhury, A. K. (2013) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2234722 · Cited by 121
Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark
Abdel-Salam, R., Mostafa, R., & Hadhood, M. (2021) · Communications in Computer and Information Science · doi:10.1007/978-981-16-0575-8_1 · Cited by 60
Metadata
- Authors
- Gorelick, L., Blank, M., Shechtman, E., Irani, M., & Basri, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-12
- Volume
- 29
- Issue
- 12
- Pages
- 2247-2253
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2146634731
- Cited by
- 1,430
- References
- 0
- FWCI
- 81.60
- Type
- journal article
- Language
- en
- Open access
- closed
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