Spiking motion direction through object motion sensitivity

Unlike conventional frame-based imaging, neuromorphic sensing mimics the brain's asynchronous processing. Event-driven cameras capture only changes in light intensity, providing microsecond-scale temporal resolution-crucial for tracking satellites amidst celestial motion and atmospheric interference. This sparse encoding significantly reduces data volume while preserving a high dynamic range, making it well-suited for challenging lighting conditions and enabling motion-blur-free imaging. The combination of event-driven input and spiking neural networks (SNNs), deployed on non-Von Neumann neuromorphic hardware, further supports real-time, adaptive processing with ultra-low power consumption, often in the milliwatt range. To distinguish moving objects while the event-based camera itself is in motion, the agent requires a robust object motion segmentation (OMS) mechanism. Building on the work of D'Angelo et al., this study proposes an SNN architecture for OMS in dynamic scenes, coupled with an additional SNN module for motion direction detection.