  Neuromorphic event-based dynamic vision sensors (DVS) have much faster
sampling rates and a higher dynamic range than frame-based imagers. However,
they are sensitive to background activity (BA) events which are unwanted. we
propose a new criterion with little computation overhead for defining real
events and BA events by utilizing the global space and time information rather
than the local information by Gaussian convolution, which can be also used as a
filter. We denote the filter as GF. We demonstrate GF on three datasets, each
recorded by a different DVS with different output size. The experimental
results show that our filter produces the clearest frames compared with
baseline filters and run fast.
