Enhancing Women's Safety in Smart Transportation Through Human-Inspired Drone-Powered Machine Vision Security

Abstract

In today's rapidly evolving transportation landscape, ensuring the safety of women has become a paramount concern. The integration of machine vision with drone-based surveillance forms a symbiotic relationship. Drones, equipped with cameras and sensors, can provide a dynamic and comprehensive view of transportation hubs, routes, and public spaces. The visual data collected by drones are instantly relayed to the machine vision algorithms, where they undergo real-time analysis. This process involves identifying patterns, anomalies, and potential threats within the transportation environment. By learning from human perception and behaviour patterns, the system can distinguish between ordinary activities and potential risks. The system can trigger immediate alerts to relevant authorities, initiating timely intervention. Additionally, the system can activate targeted deterrents, such as lights or alarms, to discourage malicious activities. This proactive and responsive approach transforms the passive security infrastructure into an active one that actively protects women's safety.