Video Object Detection for Police Surveillance using Deep Learning

Abstract

Human vision is incredibly excellent and complex. In the previous years, people made significantly more leaps to expanding this visual capacity to machines. Cameras have been used as the eyes of computers.In response to increasing anxieties about crime and its threat to security and safety, the utilization of substantial numbers of closed-circuit television system (CCTV) in both public and private spaces have been considered a necessity. The use of these significant video footages is essential to incident investigations.But as the number of these systems rises, so as the need for human operator monitoring tasks.Unfortunately, many actionable incidents are utterly undetected in this manual systemdue to inherent limitations from deploying solely human operators eye-balling CCTV screens.As a result, surveillance footages are often used merely as passive records or as evidence for post-event investigations. This study aimed to develop a real-time firearm detection using deep learning embedded in CCTV cameras that pushes alert notifications to both iOS and Android mobile devices.This research used a descriptive design and asked IT experts to evaluate the develop system based on its compliance to ISO 25010 standard. Moreover, confusion matrix and intersection over union (IoU) were used to evaluate the performanceof the system.The detection system was found to be highly recommended in urban areas particularly for CCTVs found in barangay streets and establishments.