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Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
Abstract
We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.
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Cited by
Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W., Luo, W., Lian, D., & Gao, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00684 · Cited by 1,530
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00954 · Cited by 1,107
Anomaly Detection and Localization in Crowded Scenes
Li, W., Mahadevan, V., & Vasconcelos, N. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.111 · Cited by 1,017
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
Luo, W., Liu, W., & Gao, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.45 · Cited by 895
Metadata
- Authors
- Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-03
- Volume
- 30
- Issue
- 3
- Pages
- 555-560
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2164261375
- Cited by
- 918
- References
- 0
- FWCI
- 38.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008). Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.70825
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