Abstract
As an alternative to the tracking-based approaches that heavily
depend on accurate detection of moving objects, which often fail
for crowded scenarios, we present a pixelwise method that employs
dual foregrounds to extract temporally static image regions.
Depending on the application, these regions indicate objects that
do not constitute the original background but were brought into
the scene at a subsequent time, such as abandoned and removed
items, illegally parked vehicles. We construct separate long- and
short-term backgrounds that are implemented as pixelwise
multivariate Gaussian models. Background parameters are adapted
online using a Bayesian update mechanism imposed at different
learning rates. By comparing each frame with these models, we
estimate two foregrounds. We infer an evidence score at each pixel
by applying a set of hypotheses on the foreground responses, and
then aggregate the evidence in time to provide temporal
consistency. Unlike optical flow-based approaches that smear
boundaries, our method can accurately segment out objects even if
they are fully occluded. It does not require on-site training to
compensate for particular imaging conditions. While having a
low-computational load, it readily lends itself to parallelization
if further speed improvement is necessary.