M. Murugesan*, S. Thilagamani
Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 389-405, 2022, DOI:10.32604/iasc.2022.024641
Abstract Automatic anomaly activity detection is difficult in video surveillance applications due to variations in size, type, shape, and objects’ location. The traditional anomaly detection and classification methods may affect the overall segmentation accuracy. It requires the working groups to judge their constant attention if the captured activities are anomalous or suspicious. Therefore, this defect creates the need to automate this process with high accuracy. In addition to being extraordinary or questionable, the display does not contain the necessary recording frame and activity standard to help the quick judgment of the parts’ specialized action. Therefore, to reduce the wastage of time… More >
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