Abstract
Internet of Things (IoT) has rapidly developed in multidisciplinary research topics, particularly in Cyber-Physical infrastructures, such as smart-health care, transportation systems, vehicle management surveillance systems. The smart-video surveillance system has become an essential part of almost all security applications, including academic institutions. University campuses have rich video repositories comprising almost all kinds of academic and non-academic activities. Researchers have introduced many state-of-art activity recognition methods for various application domains with the availability of several activity data sets. Unfortunately, none of these data sets or methods have been developed explicitly for academia and do not cover academic activities. With the advancement of deep learning and IoT, the processing of large-scale video data has become convenient for performing various video analysis tasks. Thus, in this work, an automated deep learning-based academic activities recognition system is presented in smart-cyber infrastructure. We explore a new academic campus domain for research and proposed a novel Convolutional Neural Network (CNN) model for academic activities recognition utilizing a realistic campus dataset. The video database typically contains long, 24-hour video streams recorded by surveillance cameras installed in campus environments. The proposed model's efficiency is tested through extensive experimentation in terms of accuracy, computation time, and memory requirement. The experimental results reveal that the proposed method attains good results with an accuracy of 98%.