  Neuromorphic event-based dynamic vision sensors (DVS) have much faster
sampling rates and a higher dynamic range than frame-based imaging sensors.
However, they are sensitive to background activity (BA) events that are
unwanted. There are some filters for tackling this problem based on
spatio-temporal correlation. However, they are either memory-intensive or
computing-intensive. We propose \emph{SeqXFilter}, a spatio-temporal
correlation filter with only a past event window that has an O(1) space
complexity and has simple computations. We explore the spatial correlation of
an event with its past few events by analyzing the distribution of the events
when applying different functions on the spatial distances. We find the best
function to check the spatio-temporal correlation for an event for
\emph{SeqXFilter}, best separating real events and noise events. We not only
give the visual denoising effect of the filter but also use two metrics for
quantitatively analyzing the filter's performance. Four neuromorphic
event-based datasets, recorded from four DVS with different output sizes, are
used for validation of our method. The experimental results show that
\emph{SeqXFilter} achieves similar performance as baseline NNb filters, but
with extremely small memory cost and simple computation logic.
