QUITE WATCH: REAL-TIME PUBLIC TRANSPORT SAFETY DETECTION

Author

Ms. S. H. Chaflekar, Prachi Nichat, Sakshi Channe, Khushi Meshram, Sejal Dantulwar


Abstract

 Public transportation systems carry large numbers of passengers, yet an emergency inside a vehicle may go unnoticed until a passenger raises an alarm or an operator reviews recorded footage. This paper presents Quiet Watch, a proposed intelligent passenger-safety monitoring system intended to support earlier identification and review of potentially unsafe events. The design accepts live or recorded video, samples frames, applies an AI-based event-analysis module, and forwards candidate incidents to an alert engine and a web dashboard. The planned software stack comprises React.js for the user interface, Node.js and Express.js for APIs, MongoDB for incident records, and Python with OpenCV and a suitable machine-learning model for video processing. Alerts are assigned a priority and presented to an authorised human operator, who can review evidence, confirm or dismiss an event, and maintain an audit trail. A citizen-reporting option can supplement camera-based detection where appropriate. The paper describes the problem, objectives, related technical foundations, architecture, evidence management, safeguards, limitations and future work. Quiet Watch is presented as a system design and prototype direction; no claim of field-proven accuracy or real-world emergency response performance is made. Evaluation using representative, consented data and human review is required before operational deployment.


Keywords

Passenger safety, public transportation, computer vision, anomaly detection, real-time alerting, incident management, privacy, human-in-the-loop AI



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