The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
AnomalyNet: An Anomaly Detection Network for Video Surveillance
Abstract
Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features, we design a motion fusion block accompanied by a feature transfer block to enjoy the advantages of eliminating noisy background, capturing motion, and alleviating data deficiency. Furthermore, to address some disadvantages (e.g., nonadaptive updating) of the existing sparse coding optimizers and embrace the merits of neural network (e.g., parallel computing), we design a novel recurrent neural network to learn sparse representation and dictionary by proposing an adaptive iterative hard-thresholding algorithm (adaptive ISTA) and reformulating the adaptive ISTA as a new long short-term memory (LSTM). To the best of our knowledge, this could be one of the first works to bridge the$\ell _{1}$ -solver and LSTM and may provide novel insight into understanding LSTM and model-based optimization (or named differentiable programming), as well as sparse coding-based anomaly detection. Extensive experiments show the state-of-the-art performance of our method in the abnormal events detection task.
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Cites
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Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
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Cited by
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
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
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“To classify abnormal activities, it is common to identify the area of interest and to evaluate activities as normal or abnormal using a specific method [120–122].”
BackgroundMethodTransCNN: Hybrid CNN and transformer mechanism for surveillance anomaly detection
Ullah, W., Hussain, T., Ullah, F. U. M., Lee, M. Y., & Baik, S. W. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106173 · Cited by 128
Metadata
- Authors
- Zhou, J. T., Du, J., Zhu, H., Peng, X., Liu, Y., & Goh, R. S. M.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2019-10
- Volume
- 14
- Issue
- 10
- Pages
- 2537-2550
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2915683453
- Cited by
- 363
- References
- 62
- FWCI
- 19.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhou, J. T., Du, J., Zhu, H., Peng, X., Liu, Y., & Goh, R. S. M. (2019). AnomalyNet: An Anomaly Detection Network for Video Surveillance. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2019.2900907
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