Event-based human intrusion detection in UAS using Deep Learning

Automatic intrusion detection in unstructured and complex environments using autonomous Unmanned Aerial Systems (UAS) poses perception challenges in which traditional techniques are severely constrained. Event cameras have high temporal resolution and dynamic range, which make them robust against motion blur and lighting conditions. This paper presents an event-by-event processing scheme for detecting human intrusion using UAS. It includes: 1) one method for detecting clusters of events caused by moving objects in static background; and 2) one method based on Convolutional Neural Networks to compute the probability that a cluster corresponds to a person. The proposed scheme has been implemented and validated in challenging scenarios.