SCOPE and motivation

Neuromorphic vision sensors, also known as event cameras, are a class of imaging devices mimicking biological visual systems. Unlike traditional frame-based cameras, which capture images synchronously, neuromorphic sensors continuously generate events capturing asynchronous illumination changes.

Event cameras have initially gained interest in the field of robotics due to their low power consumption, extremely low latency, high dynamic range and absence of motion blur. Yet, this wide range of intriguing properties has rapidly enabled new, cutting-edge applications, especially for motion-centric tasks. The very fine temporal granularity of event cameras allows to easily capture complex temporal dynamics in a scene, so that the tackling of complex tasks can abstract from the low-level processing, and focus directly on higher-level cognition.

In the past few years, we have witnessed the development of new astonishing technologies based on neuromorphic vision: low latency and low power consumption have allowed drones to effectively avoid fast-moving obstacles; high dynamic range and lack of motion blur allowed self-driving cars to detect other vehicles and pedestrians in adverse conditions such as low illumination; micro-second temporal granularity has enhanced the analysis of human micro-expressions and emotions. Many other groundbreaking applications are leveraging neuromorphic sensors, from high-speed object counting and defect detection to vibration measurement, fluid monitoring and time-to-contact estimation for spacecraft landing. Event-based processing has also been shown to provide an extra layer of privacy preservation compared to standard cameras, an important addition especially in light of the recent definition of the AI Act by the European Commission to regulate the development of artificial intelligence.