A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery

TL;DR

This paper presents a cost-effective simulation system that generates realistic wide-area aerial surveillance imagery for urban environments, aiding development and benchmarking of surveillance algorithms.

Contribution

It introduces a novel simulation approach combining urban mobility and image generation to produce realistic, large-scale surveillance datasets with ground-truth annotations.

Findings

The system effectively simulates complex urban scenes with natural traffic flows.

Generated datasets support vehicle tracking and anomaly detection algorithm testing.

The approach offers a scalable, cost-efficient alternative to real data collection.

Abstract

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.