Detecting Criminal Activities and Promoting Safety Using Deep Learning

Abstract

Automation and autonomous systems are among the few powerhouses of innovation that drive entire domains towards advancing further in leaps and bounds. Great technological innovations can be attributed to tasks that are made easier and more perceptible by automation, and artificial intelligence is here to make these automated systems smart enough to perform their tasks with the power of decision-making, thereby greatly reducing human intervention in redundant processes. Our project follows the aforementioned ideals: building a product to minimize manual labor (both physical and mental) for tasks that can be seamlessly automated and processed while solving the main problem statement at hand. Currently, surveillance cameras play a vital role to ensure the safety of the people, yet they are plain video-providing entities with no smart decision making mechanisms of their own. Because of this growth of data composed from surveillance cameras, automated video streams have become a requisite for automatically detecting abnormal events. The main aim of the project focuses on promoting safety on campus by employing deep learning techniques to automate the task of monitoring and reporting crimes from the physical Closed-Circuit Television (CCTV), assigning the responsibility of detecting criminal activity to a framework that can identify patterns to differentiate them for smarter monitoring. The model in this paper that we propose was able to distinguish between certain crimes with a 0.94 and 0.95 precision for Assault and Abuse respectively.