The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Detecting and localizing the anomalies in video surveillance using deep neuralnetwork with advanced feature descriptor
Abstract
The anomaly detection in the video surveillance is the need of the hour to detect the anomalous events or objects under the emergency conditions. The main aim of the research is to develop an automatic anomaly detection strategy for the detection of anomaly in surveillance videos. Initially, the input surveillance video is subjected to object detection using threshold method and the object tracking is performed using the Minimum output sum of squared error tracking algorithm (MOSSE). Once the objects are tracked, the statistical and advanced texture features are extracted using the statistical models and advanced texture descriptor. Then, the feature vector with statistical and textual features is fed as input to the deep convolutional neural network (DCNN) classifier that classifies the video as normal or abnormal. If any anomalies are detected in the surveillance video, the object localization is done to track the location of the abnormal object in the video. The comparative analysis of the methods reveals the better performance of the deep CNN classifier with a higher accuracy of 92.15%.
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“DCNNs can classify images even under varying levels of brightness, and they utilize advanced feature descriptors to enhance their classification performance.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“Meanwhile, the accuracy rates of other methods such as KNN, SVM, NN, and CNN were 80.97%, 80.02%, 87.20%, and 90.65%, respectively.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“The utilization of DCNN for tracking anomaly movements in low-light environments resulted in an impressive accuracy of 92.15%.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“Meanwhile, the accuracy rates of other methods such as KNN, SVM, NN, and CNN were 80.97%, 80.02%, 87.20%, and 90.65%, respectively.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“Meanwhile, the accuracy rates of other methods such as KNN, SVM, NN, and CNN were 80.97%, 80.02%, 87.20%, and 90.65%, respectively.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“Meanwhile, the accuracy rates of other methods such as KNN, SVM, NN, and CNN were 80.97%, 80.02%, 87.20%, and 90.65%, respectively.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
In the citing paper
“In low-brightness environments, DaCoLT's accuracy rate slightly lags behind that of system employing DCNN [122] for anomaly motion detection.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [122]
Citations
Cites
1 retracted
REMOVED: Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency
Al-Gabalawy, M. A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106715 · Cited by 5
RetractedChandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Visual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70825 · Cited by 920
Abnormal Event Detection in Videos Using Spatiotemporal Autoencoder
Chong, Y. S., & Tay, Y. H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59081-3_23 · Cited by 646
Enhanced Network Anomaly Detection Based on Deep Neural Networks
Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018) · IEEE Access · doi:10.1109/access.2018.2863036 · Cited by 507
Localizing Anomalies From Weakly-Labeled Videos
Lv, H., Zhou, C., Cui, Z., Xu, C., Li, Y., & Yang, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3072863 · Cited by 206
Motion-Aware Feature for Improved Video Anomaly DetectionPreprint
Zhu, Y., & Newsam, S. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.10211 · Cited by 150
Two-Stream Collaborative Learning With Spatial-Temporal Attention for Video Classification
Peng, Y., Zhao, Y., & Zhang, J. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2808685 · Cited by 131
Deep learning approaches for video-based anomalous activity detection
Pawar, K., & Attar, V. (2018) · World Wide Web · doi:10.1007/s11280-018-0582-1 · Cited by 128
Flagged first, then by citations
Research works printed but not linked: 4
- 10.3390/jimaging4020036 · DOI
- 0, UCSD Dataset
- 0, Avenue dataset
- 0, UCSD Dataset
Cited by
Sorted by citations
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
Metadata
- Authors
- Gayal, B. S., & Patil, S. R.
- Affiliations
- JSP M's RajarshiShahu College of Engineering,Dept. of Electronics & Telecommunication Engineering,Pune-33,India
- Bharati Vidyapeeth (Deemed to be University) (IN)
- Venue
- 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)
- Publisher
- IEEE
- Published
- 2022-01-28
- Pages
- 1-9
- OpenAlex
- W4223967177
- Cited by
- 3
- References
- 20
- Citations by year
- 2025: 1 · 2023: 2
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- video surveillance, anomaly detection, object localization, object tracking, texture features, statistical features, convolutional neural networks
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