The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Modelling Crowd Scenes for Event Detection
Open accessAbstract
This work presents an automatic technique for detection of abnormal events in crowds. Crowd behaviour is difficult to predict and might not be easily semantically translated. Moreover it is difficulty to track individuals in the crowd using state of the art tracking algorithms. Therefore we characterise crowd behaviour by observing the crowd optical flow and use unsupervised feature extraction to encode normal crowd behaviour. The unsupervised feature extraction applies spectral clustering to find the optimal number of models to represent normal motion patterns. The motion models are HMMs to cope with the variable number of motion samples that might be present in each observation window. The results on simulated crowds demonstrate the effectiveness of the approach for detecting crowd emergency scenarios
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Citations
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 1×
“The application domains for these techniques usually involve simple environments such as offices [8], kitchens [3] , cargo bays [6] and loading docks [5] such that activity recognition is focused upon modeling the actions and interactions of small groups of people/objects.”
Background
Cited by
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
Vision-Based Analysis of Small Groups in Pedestrian Crowds
Ge, W., Collins, R. T., & Ruback, R. B. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.176 · Cited by 358
Crowd Analysis Using Computer Vision Techniques
Silveira Jacques Junior, J., Musse, S., & Jung, C. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.937394 · Cited by 343
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
Physics Inspired Methods for Crowd Video Surveillance and Analysis: A Survey
Zhang, X., Yu, Q., & Yu, H. (2018) · IEEE Access · doi:10.1109/access.2018.2878733 · Cited by 70
Metadata
- Authors
- Andrade, E. L., Blunsden, S., & Fisher, R. B.
- Venue
- 18th International Conference on Pattern Recognition (ICPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 175-178
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2117734404
- Cited by
- 260
- References
- 1
- FWCI
- 9.33
- Type
- conference paper
- Language
- en
- Open access
- green
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
