A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery
This work addresses the scarcity and high cost of WAAS datasets for researchers developing and benchmarking aerial surveillance algorithms.
The paper presents a system for generating synthetic Wide Area Aerial Surveillance (WAAS) imagery by fusing SUMO traffic simulation with MATLAB and an image generator, enabling cost-effective dataset creation for vehicle tracking and anomaly detection. The approach produces realistic urban scenes with natural traffic flows and provides ground-truth data for algorithm evaluation.
The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a cost-effective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This `pattern-of-life' approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems.