A DRONE-BASED CRIME SURVEILLANCE FRAMEWORK USING PYTHON AND REAL-TIME DATA PROCESSING: A CONCEPTUAL STUDY

Abstract

Conventional policing struggles to stay ahead of escalating crime rates and criminals using increasingly advanced techniques. To improve crime detection and response, I propose a methodology in this paper that include drone technology with a Python programming base. The overarching strategy herein, involves patrolling defined locales and capturing real-time information with drones equipped with cameras, GPS, and sensors. The information collected will be processed using Python libraries such as OpenCV which includes video that will permit facial recognition and object identification, and the use of machine learning models will be used to locate suspicious movements or match faces with a criminal database and to issue notifications with location information immediately determined upon a match or threat being identified. Non-edited content B-1-18 The ultimate goal is providing real-time insight, faster answers, and less human effort. This article will speak to the resources that will be used, how the hardware and software will coexist, and how it could be scaled or improved in the coming years. All in all, this methodology provides a more proactive, modern approach to combat crime with a higher rate of success, and the ability to recognize new technologies as they advance.