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Detecting unusual activity in video
Open accessAbstract
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.
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Research works printed but not linked: 15
- 10.1109/CVPR.2001.990935 · DOI · Paper page
- 10.1109/34.868677 · DOI · Paper page
- 10.1109/34.598236 · DOI · Paper page
- naphade, 2000, A probabilistic framework for semantic indexing and retrieval in video, IEEE International Conference on Multimedia and Expo, 10.1109/ICME.2000.869642 · doi:10.1109/icme.2000.869642
- 10.1109/34.868688 · DOI · Paper page
- 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 · DOI · Paper page
- davis, 1997, The representation and recognition of action using temporal templates, IEEE Conference on Computer Vision and Pattern Recognition, 928, 10.1109/CVPR.1997.609439 · DOI · Paper page
- 10.1109/CVPR.1997.609450 · DOI · Paper page
- 10.1109/ICCV.1999.791201 · DOI · Paper page
- 10.1016/0262-8856(96)01101-8 · DOI · Paper page
- 10.1109/AFGR.1998.670952 · DOI · Paper page
- 10.1109/ICCV.2003.1238420 · DOI · Paper page
- 10.1145/502512.502550 · DOI · Paper page
- 10.1109/34.946990 · DOI · Paper page
- 10.1101/gr.648603 · DOI · Paper page
Cited by
End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585
Online detection of unusual events in videos via dynamic sparse coding
Zhao, B., Fei-Fei, L., & Xing, E. P. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995524 · Cited by 532
A comprehensive review on deep learning-based methods for video anomaly detection
Nayak, R., Pati, U. C., & Das, S. K. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104078 · Cited by 324
Efficient duration and hierarchical modeling for human activity recognition
Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 115
Metadata
- Authors
- Zhong, H., Shi, J., & Visontai, M.
- Venue
- Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
- Publisher
- IEEE
- Published
- 2004-11-12
- Volume
- 2
- Pages
- 819-826
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2124658620
- Cited by
- 495
- References
- 0
- FWCI
- 17.78
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhong, H., Shi, J., & Visontai, M. (2004). Detecting unusual activity in video. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. https://doi.org/10.1109/cvpr.2004.1315249
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