Blink and you’ll miss it: event-based data synthesis for fast-moving object detection in constrained platforms

Detecting fast-moving objects in dynamic scenes is a fundamental challenge in event-based vision, with applications ranging from terrestrial surveillance to space-based tracking of Resident Space Objects (RSOs). Conventional frame-based imaging and convolutional pipelines struggle to meet the latency and power requirements of resource-constrained platforms. In this work, we present a scalable method for generating labeled training data by superimposing real event streams from stationary-camera foreground recordings and ego-motion background recordings, and demonstrate that a lightweight CNN trained on this data generalizes to real, unmodified event streams. This provides a practical foundation for future deployment in autonomous detection systems, including space-based applications where event cameras are well-suited to the constraints of spacecraft payloads.