Article
ANOMALY DETECTION USING VGG 16 ARCHITECTURE
There has been a recent uptick in the installation of high-tech video surveillance equipment in public areas. One of the primary uses for gathered video features is safety monitoring, made possible by the implementation of deep learning and machine learning techniques. In this work, our primary focus is on anomaly detection in situations with a large number of people, both indoors and outdoors. In this study, we describe the VGG 16 architecture for the detection of abnormalities occurring in surveillance cameras using classifiers. The VGG 16 architecture was implemented with state-of-the-art classifiers like random forest, J48, decision tree, and SVM. Experiments revealed that the suggested model of using VGG 16 has a relatively low computational burden while still producing satisfactory results, with an accuracy of 80.86% for the random forest classifier.
Full Text Attachment
