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Recognizing human actions: a local SVM approach
Abstract
Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.
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Cited by
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117
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
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
Metadata
- Authors
- Schuldt, C., Laptev, I., & Caputo, B.
- Venue
- Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
- Publisher
- IEEE
- Published
- 2004
- Pages
- 32-36 Vol.3
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W4249279051
- Cited by
- 3,010
- References
- 0
- FWCI
- 10.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: a local SVM approach. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. https://doi.org/10.1109/icpr.2004.1334462
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