Artificial Intelligence-Based Surveillance System for Railway Crossing Traffic

The application of Artificial Intelligence (AI) based techniques has strong potential to improve safety and efficiency in data-driven Intelligent Transportation Systems (ITS) as well as in the emerging Internet of Vehicles (IoV) services. This paper deals with the practical implementation of deep learning methods for increasing safety and security in a specific ITS scenario: railway crossings. This research work presents our proposed system called Artificial Intelligence-based Surveillance System for Railway Crossing Traffic (AISS4RCT) that is based on a combination of detection and classification methods focusing on various image processing inputs: vehicle presence, pedestrian presence, vehicle trajectory tracking, railway barriers at railway crossings, railway warnings, and light signaling systems. The designed system uses cameras that are suitably positioned to capture an entire crossing area at a given railway crossing. By employing GPU accelerated image processing techniques and deep neural networks, the system autonomously detects risky and dangerous situations at railway crossing in real-time. In addition, camera modules send data to a central server for further processing as well as notification to interested parties (police, emergency services, railway operators). Furthermore, the system architecture employs privacy-by-design and security-by-design best practices in order to secure all communication interfaces, protect personal data, and to increase personal privacy, i.e., pedestrians, drivers. Finally, we present field-based results of detection methods, and using the YOLO tiny model method we achieve average recall 89%. The results indicate that our system is efficient for evaluating the occurrence of objects and situations, and it's practicality for use in railway crossings.