Anomaly Detection in Videos for Video Surveillance Applications using Neural Networks
Abstract
Security is always a main concern in every domain, due to a rise in crime rate in the crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, the needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. Anomaly detection is a technique used to distinguish various patterns and identify unusual patterns with a minimal period, this pattern is called outliers. Surveillance videos can capture a variety of realistic anomalies. Anomaly detection in video surveillance involves breaking down the whole process into three layers, which are video labelers, image processing, and activity detection. Hence, anomaly detection in videos for video surveillance application gives assured results in regards to real-time scenarios. In this paper, we anomaly was detected in images and videos with an accuracy of 98.5 %.
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In [121], a CNN for feature extraction and MRCNN for further classification achieved an average accuracy of 97.5% in 2020.
“In [121], a CNN was initially used to extract features, and a MRCNN is employed for further classification with an average accuracy of 97.5% in 2020.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [121]
MRCNN can be used in areas where many people and cars gather because all objects recorded in the video frame can be semantically segmented one by one.
“MRCNN can be used in areas where many people and cars gather, because all the objects recorded in the video frame can be semantically segmented one by one.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [121]
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Method Reason for False Alarm [126] Deep learning Alarms are recognized as abnormal when unknown or new normality appears [121] Neural network What is considered an anomaly today may not be considered an anomaly tomorrow due to the lack of data [161] Deep learning Post-incident alarm triggering…”
BackgroundMethodInfluential
- Authors
- Franklin, R. J., Mohana, & Dabbagol, V.
- Affiliations
- R.V. College of Engineering (IN)
- ORCID
- Mohana
- Venue
- 2020 Fourth International Conference on Inventive Systems and Control (ICISC)
- Publisher
- IEEE
- Published
- 2020-01
- Pages
- 632-637
- OpenAlex
- W3081509497
- Cited by
- 79
- References
- 27
- Citations by year
- 2026: 1 · 2025: 13 · 2024: 17 · 2023: 21 · 2022: 9 · 2021: 14 · 2020: 4
- FWCI
- 4.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Inventive Systems and Control (ICISC) (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 › Human Pose and Action Recognition
- Keywords
- video anomaly detection, video surveillance, artificial neural networks, outlier detection, activity detection, real-time monitoring, security
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