Auto Deep Learning-based Automated Surveillance Technique to Recognize the Activities in the Cyber-Physical System

In recent days, the Internet of Things (IoT) plays a significant role and increasing in rapid usage in various applications. As IoT is being developed for cyber-physical systems in the specific domain of e-health care, military, etc. Based on real-time applications, security plays a vital role in certain activities in educational institutions. In the institutions, there are multiple videos are collected and stored in the data repositories. Those datasets are developed specifically for certain activities and no other datasets are developed for academic activities. As there is a large number of videos and images are collected and considered, advanced technologies like, deep learning and IoT are used to perform certain tasks. In this paper, a Auto Deep learning-based Automated Identification Framework (DLAIF) is proposed to consider and reconsider the activities based on image pre-processing, model can be trained through the proposed GMM model and then predication to make an effective surveillance process based on HMM. This proposed process makes to recognize the activities through EM and log Likelihood for cyber-physical systems. In the performance analysis, the proposed model efficiency can be determined through Accuracy detection, False Positive rate and F1 Score requirement. Then calculating the accuracy is more effective for the proposed model compared to other existing models such as BWMP and LATTE