A New Hybrid CNN Model for Abnormal Behaviour Detection in Smart Campus

Security and monitoring systems are provided through video cameras in public places such as universities, hospitals, cinemas and theater halls. In smart campuses created by the integration of various sensors and digital technologies, there are many video cameras indoors and outdoors in the campus areas to ensure the safety of students, academics, administrators and visitors. Examining the images recorded with these video cameras in terms of security and detecting abnormal behaviours requires a long time and effort. In this study, a new hybrid convolutional neural network (CNN) model is proposed to detect abnormal behaviors occurring in smart campus areas. In the proposed method, firstly, the features of the UTI public dataset, which includes various student behaviors, are extracted with the hybrid use of InceptionV3 and MobileNetV2 deep transfer learning models. The extracted features of the images were combined and classified by converting them into a single feature vector. 6 different behavior classes, 3 of which are normal and 3 of which are abnormal, have been classified and detected with 92.7 % accuracy. Experimental results show that the proposed method for abnormal behavior detection produces more efficient results than other methods in terms of feature extraction, accuracy and computational costs.