1 research outputs found
Exploiting an event based state estimator in presence of sparse measurements in video analytics
Recently, a Bayesian estimator with a hybrid update was developed
[1], based on a mathematical formulation of sampling. Such an
Event Based State Estimator (EBSE) allows for a stable synchronous
state estimate, relying on asynchronous measurements. Usefulness
of such a filter comes with its approximate analytic formulation,
which is attainable given a send-on-delta sampling strategy. We argue
that such a formulation can be extended to cope with a failing
detector in case the filter is used for tracking. The basic idea is to
approach the issue as a package loss problem, where a missed target
is assimilated to a lost package. More in detail, we propose that this
approach can be exploited in video tracking, where faulty detectors
are commonplace. We show how tracking performance with a poor
pedestrian detector, failing to recognize its target, can improve with
respect to standard Kalman filter