The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Review of Video Analytics Method for Video Surveillance
Abstract
To alleviate increase rate of crime, security personnel, IP camera, CCTV (Closed-Circuit Television) is traditionally employed. But human vigilance leads to errors. Hence, automated video surveillance and analytics are implemented to minimize these errors. Human behaviour recognition algorithms can be used reactively to prevent issues or proactively for post-incident inquiry. There is increasing demand in automated video surveillance, which involves face detection, object detection, license plate detection. This study provides the most recent state-of-the-art image processing algorithms for automatic behaviour detection approaches, with an emphasis on human activity monitoring.
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In the citing paper
“In the context of video security, the use of private or permissioned blockchains instead of a public blockchain is recommended, as private blockchains use control mechanisms to regulate access to videos to ensure that the blockchain network is only accessible to authorized participants [135].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [135]
Citations
Cites
2 retracted
Sridhar, P., & Sathiya, R. R. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1916/1/012027 · Cited by 6
RetractedFacial Emotion Recognition Using Deep Learning
Vimal, K. U., Sandij, S. K., Yogesh, M., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1916/1/012118 · Cited by 3
RetractedJoint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics
Wang, C., Zhang, S., Chen, Y., Qian, Z., Wu, J., & Xiao, M. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155524 · Cited by 240
Spatio-Temporal Attention Networks for Action Recognition and Detection
Li, J., Liu, X., Zhang, W., Zhang, M., Song, J., & Sebe, N. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2965434 · Cited by 181
Object Detection Using Deep Learning Methods in Traffic Scenarios
Boukerche, A., & Hou, Z. (2021) · ACM Computing Surveys · doi:10.1145/3434398 · Cited by 137
Edge Coordinated Query Configuration for Low-Latency and Accurate Video Analytics
Yang, P., Lyu, F., Wu, W. R., et al. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2949347 · Cited by 99
Sequential and Patch Analyses for Object Removal Video Forgery Detection and Localization
Aloraini, M., Sharifzadeh, M., & Schonfeld, D. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2993004 · Cited by 99
A novel keyframe extraction method for video classification using deep neural networks
Kızıltepe, R. S., Gan, J. Q., & Escobar, J. J. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06322-x · Cited by 84
CNN spatiotemporal features and fusion for surveillance video forgery detection
Fadl, S. M., Han, Q., & Li, Q. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116066 · Cited by 69
Decomposable Intelligence on Cloud-Edge IoT Framework for Live Video Analytics
Zhang, Y., Liu, J.-H., Wang, C.-Y., & Wei, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2997091 · Cited by 60
Research works printed but not linked: 1
- janakiramaiah, 2020, Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm, Evolutionary Intelligence, 1
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
“In the context of video security, the use of private or permissioned blockchains instead of a public blockchain is recommended, as private blockchains use control mechanisms to regulate access to videos to ensure that the blockchain network is only accessible to authorized participants [135].”
Background
Metadata
- Authors
- Jayamohan, M., Yuvaraj, S., & Vijayakumar, P.
- Affiliations
- SRM Institute of Science and Technology (IN)
- Venue
- 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)
- Publisher
- IEEE
- Published
- 2022-02-11
- Pages
- 43-47
- OpenAlex
- W4281748977
- Cited by
- 6
- References
- 21
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 1
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Recent Trends in Computer Science and Technology (ICRTCST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- video analysis, automated video surveillance, human behavior recognition, human activity monitoring, face detection, object detection, license plate detection, CCTV, crime prevention
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Review of Video Analytics Method for Video Surveillance. https://app.cochat.ai/papers/10.1109/icrtcst54752.2022.9782005
- The paper
- Jayamohan, M., Yuvaraj, S., & Vijayakumar, P. (2022). Review of Video Analytics Method for Video Surveillance. 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). https://doi.org/10.1109/icrtcst54752.2022.9782005
