UC San Diego

Neuromorphic vision sensors are an emerging technology inspired by how retina processing images. A neuromorphic vision sensor only reports when a pixel value changes rather than continuously outputting the value every frame as is done in an 'ordinary' Active Pixel Sensor (ASP). This move from a continuously sampled system to an asynchronous event driven one effectively allows for much faster sampling rates; it also fundamentally changes the sensor interface. In particular, these sensors are highly sensitive to noise, as any additional event reduces the bandwidth, and thus effectively lowers the sampling rate. In this work we introduce a novel spatiotemporal filter with O(N)O(N) memory complexity for reducing background activity noise in neuromorphic vision sensors. Our design consumes 10× less memory and has 100× reduction in error compared to previous designs. Our filter is also capable of recovering real events and can pass up to 180 percent more real events.