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.