Digital video audit system for tolling

Abstract

A Digital Video Audit System for toll transaction verification, speed enforcement, and vehicle monitoring that synchronizes multiple video feeds with comprehensive vehicle metadata. The system integrates a camera assembly capturing video streams of vehicles traversing infrastructure with a server assembly that processes these streams using machine learning modules. A multi-object tracking module generates vehicle trajectories by applying deep learning object detectors and tracking algorithms, while an event correlation module associates events with these trajectories. The DVAS displays zone controller transactional data overlaid or adjacent to video data through an intuitive user interface, enabling dynamic filtering by various vehicle characteristics and transaction parameters. The system incorporates redundancy features and security measures to prevent revenue loss and ensure data integrity. By providing synchronized visual verification of toll transactions with corresponding metadata, the DVAS facilitates efficient auditing, accurate toll collection, and reporting across tolling facilities, even in challenging traffic conditions where conventional systems may fail.