Smart City Gnosys
Smart city article details
| Title | Analysis Of Anomaly Detection In Surveillance Video: Recent Trends And Future Vision |
|---|---|
| ID_Doc | 9107 |
| Authors | Raja R.; Sharma P.C.; Mahmood M.R.; Saini D.K. |
| Year | 2023 |
| Published | Multimedia Tools and Applications, 82, 8 |
| DOI | http://dx.doi.org/10.1007/s11042-022-13954-1 |
| Abstract | Video Surveillance (VS) systems are popular. For enhancing the safety of public lives as well as assets, it is utilized in public places like marketplaces, hospitals, streets, education institutions, banks, shopping malls, city administrative offices, together with smart cities. The main purpose of security applications is the well-timed and also accurate detection of video anomalies. Anomalous activities along with anomalous entities are the video anomalies, which are stated as the irregular or abnormal patterns on the video that doesn’t match the normal trained patterns. Automatic detection of Anomalous activities, say traffic rule infringements, riots, fighting, and stampede in addition to anomalous entities, say, weapons at the sensitive place together with deserted luggage ought to be done. The Anomaly Detection (AD) in VS is reviewed in the paper. This survey concentrates on the Deep Learning (DL) application in finding the exact count, involved individuals and the occurred activity on a larger crowd at every climate condition. The fundamental DL implementation technology concerned in disparate crowd Video Analysis (VA) is discussed. Moreover, it presented the available datasets as well as metrics for performance evaluation and also described the examples of prevailing VS systems utilized in the real life. Lastly, the challenges together with propitious directions for additional research are outlined. Pattern recognition has been the subject of a great deal of study during the previous half-century. There isn’t a single technique that can be utilised for all kinds of applications, whether in bioinformatics or data mining or speech recognition or remote sensing or multimedia or text detection or localization or any other area. Methodologies for object recognition are the primary focus of this paper. All aspects of object recognition, including local and global feature-based algorithms, as well as various pattern-recognition approaches, are examined here. Please note that we have attempted to describe the findings of many technologies and the future extent of this paper’s particular technique. We used the datasets’ properties and other evaluation parameters found in an easily accessible web database. Research in pattern recognition and object recognition can greatly benefit from this study, which identifies the research gaps and limits in this subject. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
| Author Keywords | Anomaly detection; Deep learning; Feature extraction; Machine learning; Real-time video detection; Video surveillance |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
937  | 0.952 | Nayak R.; Pati U.C.; Das S.K. | A Comprehensive Review On Deep Learning-Based Methods For Video Anomaly Detection | Image and Vision Computing, 106 (2021) |
| 908 | 0.943 | Abbas Z.K.; Al-Ani A.A. | A Comprehensive Review For Video Anomaly Detection On Videos | Proceedings of the 2nd 2022 International Conference on Computer Science and Software Engineering, CSASE 2022 (2022) |
| 974 | 0.927 | Choudhry N.; Abawajy J.; Huda S.; Rao I. | A Comprehensive Survey Of Machine Learning Methods For Surveillance Videos Anomaly Detection | IEEE Access, 11 (2023) |
| 9638 | 0.922 | Patrikar D.R.; Parate M.R. | Anomaly Detection Using Edge Computing In Video Surveillance System: Review | International Journal of Multimedia Information Retrieval, 11, 2 (2022) |
| 44339 | 0.922 | Samaila Y.A.; Sebastian P.; Shuaibu A.N.; Azhar Ali S.S.; Muhammad S.A.; Yahya M.S.; Shuaibu I.; Sani I.M. | Real-Time Deep Anomaly Detection: An Overview Of Benchmark Datasets And Performance Metrics | Transportation Research Procedia, 84 (2025) |
| 9627 | 0.922 | Borawar L.; Kaur R. | Anomaly Detection Methods In Surveillance Videos: A Survey | 2022 International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022 (2022) |
| 61219 | 0.905 | Ullah W.; Hussain T.; Baik S.W. | Vision Transformer Attention With Multi-Reservoir Echo State Network For Anomaly Recognition | Information Processing and Management, 60, 3 (2023) |
| 48481 | 0.9 | Ullah W.; Min Ullah F.U.; Ahmad Khan Z.; Wook Baik S. | Sequential Attention Mechanism For Weakly Supervised Video Anomaly Detection | Expert Systems with Applications, 230 (2023) |
| 22251 | 0.899 | Saleem G.; Bajwa U.I.; Raza R.H.; Alqahtani F.H.; Tolba A.; Xia F. | Efficient Anomaly Recognition Using Surveillance Videos | PeerJ Computer Science, 8 (2022) |
| 17780 | 0.899 | Sharif M.H.; Jiao L.; Omlin C.W. | Deep Crowd Anomaly Detection: State-Of-The-Art, Challenges, And Future Research Directions | Artificial Intelligence Review, 58, 5 (2025) |