The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Hierarchical spatio-temporal context modeling for action recognition
Abstract
The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases under controlled environments or focus on excessively localized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the spatio-temporal context information in a hierarchical way, where three levels of context are exploited in ascending order of abstraction: 1) point-level context (SIFT average descriptor), 2) intra-trajectory context (trajectory transition descriptor), and 3) inter-trajectory context (trajectory proximity descriptor). To obtain efficient and compact representations for the latter two levels, we encode the spatiotemporal context information into the transition matrix of a Markov process, and then extract its stationary distribution as the final context descriptor. Building on the multichannel nonlinear SVMs, we validate this proposed hierarchical framework on the realistic action (HOHA) and event (LSCOM) recognition databases, and achieve 27% and 66% relative performance improvements over the state-of-the-art results, respectively. We further propose to employ the Multiple Kernel Learning (MKL) technique to prune the kernels towards speedup in algorithm evaluation.
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Citations
Cites
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Learning realistic human actions from movies
Laptev, I., Marszalek, M., Schmid, C., & Rozenfeld, B. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587756 · Cited by 3,444
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Recognizing human actions: A local SVM approach
Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Cited by 2,908
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Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
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Cited by
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
Domain Transfer Multiple Kernel Learning
Duan, L., Tsang, I. W., & Xu, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.114 · Cited by 548
Activity recognition using the velocity histories of tracked keypoints
Messing, R., Pal, C., & Kautz, H. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459154 · Cited by 455
Better Exploiting Motion for Better Action Recognition
Jain, M., Jegou, H., & Bouthemy, P. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.330 · Cited by 385
Metadata
- Authors
- Sun, J., Wu, X., Yan, S., Cheong, L.-F., Chua, T.-S., & Li, J.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 2004-2011
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2114361838
- Cited by
- 358
- References
- 25
- FWCI
- 26.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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