The demand for greater security measures in crowded environments for monitoring and protecting
operations has made video anomaly detection significant study area in computer vision. This is due to
the fact that the detection of anomalies in video surveillance systems has increased, making it one of
the leading focus areas in the current field of research. In this study, we offer a method for identifying
out-of-the-ordinary anomaly behaviour in video footage of crowded settings. The proposed approach
uses the three CNN architectures of AlexNet, ResNet and VGGNet. Fine tunes the architectures by
using conjugate gradient optimization. The transfer learning approach is followed for the
classification. The proposed method saves time by not performing the training part again and
combines the results from the three architectures. Random forest and Softmax classifier perform the
classification. The proposed model faresbetter than some o
Keywords :
Author : KALLEPALLI ROHIT KUMAR, DR.NISARG GANDHEWAR
Title : Anomaly Detection Using Transfer Learning in CNNs
Volume/Issue : 2022;04(5)
Page No : 1-8
