AI-Based Security Surveillance Systems for Enhancing Public Transportation Safety

Authors

Ravikanth Konda

Abstract

Public transport networks are a critical component of urban mobility, providing a convenient and eco-friendly substitute for private vehicles. Yet, they are still exposed to various security risks, including vandalism, theft, physical violence, terrorism, and emergencies. Conventional CCTV-based surveillance systems often rely on a reactive approach and are limited by the capacity of human operators, rendering them inadequate for real-time threat identification and intervention. The rapid development of Artificial Intelligence (AI), including computer vision and deep learning, has enabled the creation of intelligent surveillance systems that revolutionize public safety in transit environments.
This work explores the deployment of AI-powered surveillance systems into public transportation systems with the aim of improving commuter safety, incident detection, and emergency response. Through the use of AI algorithms, these systems can identify unusual behavior, unauthorized entry, overcrowding, unattended luggage, and even potential violent acts through real-time video analytics. We suggest a scalable architecture that integrates edge computing, real-time streaming of data, machine learning inference engines, and cloud integration for post-event analytics.
The approach relies on established AI frameworks such as YOLOv5 for object detection, OpenPose for human body posture identification, and LSTM networks for predicting behavior. Case studies and performance tests carried out on public transport datasets exhibit remarkable improvements in threat detection accuracy, latency minimization, and response automation. The research also deals with main challenges like privacy issues, false positives, computational complexity, and ethical considerations.
This paper offers a strong, future-proof AI architecture for real-time surveillance in metropolitan transit systems, towards the larger vision of secure, smart, and resilient urban mobility. The findings of this research can benefit transport authorities, urban planners, and AI practitioners in developing effective surveillance mechanisms specific to metropolitan transit environments.

Keywords

AI-Based Security Surveillance Systems for Enhancing Public Transportation Safety. Ravikanth Konda. 2023. IJIRCT, Volume 9, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2505011