Big Data Enabled Realtime Crowd Surveillance and Threat Detection Using Artificial Intelligence and Deep Learning

Abstract

In recent years, security precautions at public events have become increasingly important as a direct response to the growth in the number of disruptive actions. There are several various varieties of closed-circuit televisions that are employed to perform 24-hour surveillance of public areas and the people that live such areas. Every person in a socialised society with a population of 1.6 billion is subjected to imprinted pictures an average of 30 times each day. Since the entire participation of the community is required to safeguard public areas from the most unexpected and lethal of events, it is difficult to discern whether an incident is an exceptional or casual occurrence because continual monitoring of human data makes it difficult to distinguish the difference. Within the scope of this study, we propose a method for identifying potentially threatening actions within footage obtained from closed-circuit television systems. To accomplish this objective, we must first extract individual still frames from the video and then examine the actions of the people seen in those still frames. We have placed a significant amount of reliance on both machine learning and deep learning algorithms to make this a reality. To automate this process, we must first develop a training model that makes use of many photos and a "Convolution Neural Network" that makes use of the Tensor Flow Python package. This model must be created before we can move on to automating the process. Every frame from every video that is provided will be used to train an algorithm that will analyse the film and evaluate whether it contains suspicious content or merely everyday activities. If we conclude that the activity was suspicious, the next phase of the study will concentrate on locating any weapons that may have been concealed on the corpse.