Asynchronous event-based clustering and tracking for intrusion monitoring in UAS

Automatic surveillance and monitoring using Unmanned Aerial Systems (UAS) require the development of perception systems that robustly work under different illumination conditions. Event cameras are neuromorphic sensors that capture the illumination changes in the scene with very low latency and high dynamic range. Although recent advances in event-based vision have explored the use of event cameras onboard UAS, most techniques group events in frames and, therefore, do not fully exploit the sequential and asynchronous nature of the event stream. This paper proposes a fully asynchronous scheme for intruder monitoring using UAS. It employs efficient event clustering and feature tracking modules and includes a sampling mechanism to cope with the computational cost of event-by-event processing adapting to on-board hardware computational constraints. The proposed scheme was tested on a real multirotor in challenging scenarios showing significant accuracy and robustness to lighting conditions.