Sistema de monitoreo y detección de comportamiento delincuencial aplicado en medios de transporte utilizando visión artificial

Abstract

Nowadays, the different video surveillance systems within the means of transport are largely dependent on monitoring and human control, and as a result, the costs incurred by the owners of the means of transport are high to carry out this objective. In a way, a proposal has been made for a monitoring and detection system of delinquent behavior applied to means of transport using artificial vision. The system is implemented inside an NVIDIA Jetson Orin Nano with a 720p webcam and a Neo 6m GPS module for georeferencing. The system uses libraries such as OpenCV and MediaPipe for video processing and body posture estimation, integrating AI models (YOLO and SVM) for the detection of suspicious objects and threatening postures. Events are classified as “Normal”, ‘Suspicious’ or “Flagrant Crime” based on combined thresholds. Alerts, containing captures and coordinates, are sent in real time to Slack and managed from a Flask web interface. Absolutely all detections are stored within a SQLite database for traceability and future enhancements. This system automates security monitoring, improving responsiveness and crime prevention.