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%.
Highlights
Nowadays, Internet of Things (IoT) enables the CyberPhysical components to interact with other devices and to communicate with safety-critical systems
As concluded that until now, none of the work is based on academic activity classification and recognition, in this work, we developed an automated method that focused on academic activity classification with deep learning model
EXPERIMENTAL RESULTS AND DISCUSSION The experiments are conducted on a system having Intel Core i-5 CPU @ 3.20 GHz with 24 GB RAM and NVidea GeForce GTX 1080 Ti Graphic Processing Unit (GPU)
Summary
Internet of Things (IoT) enables the CyberPhysical components to interact with other devices and to communicate with safety-critical systems. Due to the successful development of smart video surveillance technology and pervasive computing, video-based activity recognition is becoming more popular and widely used in different industrial, academic, and public environments. VOLUME 9, 2021 system is typically intended to cover all the sensitive areas such as entrances, exits, boundary walls, offices, classrooms, halls, parking, record rooms, laboratories, wallets, hostels, residences & other public places dealings These cameras produce a massive amount of video data and are stored on backup storage for future correspondence. Computer-assisted techniques for searching, abstraction, and indexing need to be developed to increase data timeliness, efficient utilization of available resources, and minimize the processing time One solution to this problem is automated activity classification in videos. In this work we presented, an efficient automated deep learning architecture for academic activity recognition in campus surveillance videos. The experimental setup, with different experimental results and findings, is provided in Section V and Section VI concludes our research and explains some future directions
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