The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes
Abstract
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN). We divide the deep autoencoder and the CNN into multiple sub-stages which operate as cascaded classifiers. Shallow layers of the cascaded deep networks (designed as Gaussian classifiers, acting as weak single-class classifiers) detect "simple" normal patches such as background patches, and more complex normal patches are detected at deeper layers. It is shown that the proposed novel technique (a cascade of two cascaded classifiers) performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
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Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Abnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206641 · Cited by 1,672
Convolutional neural networks at constrained time cost
He, K., & Sun, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299173 · Cited by 1,558
Anomaly detection in crowded scenes
Mahadevan, V., Li, W., Bhalodia, V., & Vasconcelos, N. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539872 · Cited by 1,512
Abnormal Event Detection at 150 FPS in MATLAB
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A convolutional neural network cascade for face detection
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Cited by
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
Self-citationDeep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 470
Self-citationAnomalyNet: An Anomaly Detection Network for Video Surveillance
Zhou, J. T., Du, J., Zhu, H., Peng, X., Liu, Y., & Goh, R. S. M. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2900907 · Cited by 363
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
Metadata
- Authors
- Sabokrou, M., Fayyaz, M., Fathy, M., & Klette, R.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-04
- Volume
- 26
- Issue
- 4
- Pages
- 1992-2004
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2587789887
- Cited by
- 386
- References
- 52
- FWCI
- 21.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sabokrou, M., Fayyaz, M., Fathy, M., & Klette, R. (2017). Deep-Cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2017.2670780
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