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Conference Paper
Detecting Change to Quantify Anomalies for Robust Outdoor Surveillance
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- Issue Date
- 2021-10
- Citation
- International Conference on Control, Automation and Systems (ICCAS) 2021, pp.1-3
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Autonomous surveillance systems is becoming the need of the hour. Several researchers have proposed the idea of one-class classification, in which only normal data is used to train a one-class classification system. However, such approaches are prone to sudden change in environment, background shifts, etc. In order to eradicate such issues, we proposed to utilize change detection algorithm for anomaly detection. Such algorithms are more robust, can utilize real anomaly examples and over come the shortcomings of the one-class classification methods.
- KSP Keywords
- Change Detection Algorithm, Classification method, Classification system, One-class classification(OCC), Surveillance system, anomaly detection, sudden change