Gradient events: improved acquisition of visual information in event cameras
This work addresses a specific challenge in event camera technology, offering an incremental improvement for applications requiring robust visual information acquisition in dynamic lighting conditions.
The authors tackled the problem of uninformative events from oscillating light sources in event cameras by proposing gradient events, which improved grayscale frame reconstruction and outperformed existing brightness event-based methods on public datasets.
The current event cameras are bio-inspired sensors that respond to brightness changes in the scene asynchronously and independently for every pixel, and transmit these changes as ternary event streams. Event cameras have several benefits over conventional digital cameras, such as significantly higher temporal resolution and pixel bandwidth resulting in reduced motion blur, and very high dynamic range. However, they also introduce challenges such as the difficulty of applying existing computer vision algorithms to the output event streams, and the flood of uninformative events in the presence of oscillating light sources. Here we propose a new type of event, the gradient event, which benefits from the same properties as a conventional brightness event, but which is by design much less sensitive to oscillating light sources, and which enables considerably better grayscale frame reconstruction. We show that the gradient event -based video reconstruction outperforms existing state-of-the-art brightness event -based methods by a significant margin, when evaluated on publicly available event-to-video datasets. Our results show how gradient information can be used to significantly improve the acquisition of visual information by an event camera.