The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Markov Clustering Topic Model for mining behaviour in video
Abstract
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robustness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into global behaviours, and correlates behaviours over time. A collapsed Gibbs sampler is derived for offline learning with unlabeled training data, and significantly, a new approximation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models, mining behaviours and detecting salient events in three complex and crowded public scenes.
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Cites
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.61 · Cited by 535 · cited 10×
“We compare the computational cost of our MCTM, LDA [3], Dual-HDP [13] and HMMs in Sec.”
MethodInfluential
Research works printed but not linked: 15
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- blei, 2003, Latent dirichlet allocation, JMLR, 3, 993
- griffiths, 2007, Integrating topics and syntax, NIPS, 537
- gilks, 1995, Markov Chain Monte Carlo in Practice, 10.1201/b14835 · DOI · Paper page
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Cited by
Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.09.002 · Cited by 371
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Feng, J.-C., Hong, F.-T., & Zheng, W.-S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01379 · Cited by 355
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
Zhou, B., Wang, X., & Tang, X. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248013 · Cited by 313
Das, P., Xu, C., Doell, R. F., & Corso, J. J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.340 · Cited by 282
Deeply learned attributes for crowded scene understanding
Shao, J., Kang, K., Loy, C. C., & Wang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299097 · Cited by 280
Metadata
- Authors
- Hospedales, T., Gong, S., & Xiang, T.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1165-1172
- OpenAlex
- W2539222059
- Cited by
- 239
- References
- 1
- FWCI
- 21.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hospedales, T., Gong, S., & Xiang, T. (2009). A Markov Clustering Topic Model for mining behaviour in video. 2009 IEEE 12th International Conference on Computer Vision. https://doi.org/10.1109/iccv.2009.5459342
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
