ITS-Vision: Autonomous Vehicles as Mobile Surveillance Nodes in Intelligent Transportation Systems—A Conceptual Framework and Proof-of-Concept Prototype

Crime surveillance in urban environments faces increasing challenges due to dynamic conditions and the demand for real-time monitoring. This paper investigates the use of video data from autonomous vehicles to enhance situational awareness in public spaces through deep learning models optimized for edge processing. High-resolution vehicle-mounted cameras serve as mobile surveillance units capable of real-time object detection, human action recognition, and anomaly detection, bridging the gap between autonomous mobility and urban monitoring. Building on this vision, we introduce ITS-Vision, a generic framework that operationalizes these use cases, enabling autonomous vehicles to function as mobile, context-aware sensing platforms. To validate this approach, we develop prototypes for key ITS-Vision components: a fight detection module using a fine-tuned X3D model, suspect identification via MediaPipe for detection combined with FaceNet for embedding extraction, and a dangerous items detection module using a fine-tuned YOLOv11n model. Due to the limited availability of real-world autonomous vehicle video datasets, experiments were conducted in controlled laboratory environments, demonstrating the feasibility of the proposed architecture and algorithms under simulated conditions. Future work will focus on collecting dedicated datasets and advancing the models toward deployment in real urban scenarios.