Anomalous Behaviour Detection using Transfer Learning Algorithm of Series and DAG Network
Published on Oct 1, 2019
· DOI :10.1109/ICSENGT.2019.8906470
Transfer learning is highly recommended in image recognition studies due to its ability in leveraging the finest architecture of pre-trained convolution neural networks (CNNs) for instance AlexNet, GoogLeNet and several others in learning new dataset at a faster learning process with smaller input images and could yield better classification rate as well. Hence, this study discussed deep learning with transfer learning approach in recognizing and classifying normal behavior and anomalous behavior referring to housebreaking crime behavior at the gate of the residential unit. Firstly, the dataset of normal behavior and housebreaking crime behavior are acquired. Next, these images are extracted and classified using remodeled AlexNet and GoogLeNet that have been fine-tuned using transfer learning technique. Results attained showed that the classification accuracy for both AlexNet and GoogLeNet are within 97%.