Sequential Bayesian estimation of the probability of detection for tracking

We propose a Bayesian estimation method to sequentially update the probability of detection for tracking. A beta distribution is used for the prior, which can be centered on the best a priori guess for the probability of detection. The tracker's belief about whether it detected the target at the last scan is used to update the posterior estimate of the probability of detection. The method can be applied to any tracking algorithm that requires an estimate of the probability of detection. Experiments with the probabilistic data association (PDA) tracker show that the proposed estimation method can increase the amount of time a target is tracked and decrease the localization error when compared to using a fixed value. Experiments also show that for some values of the probability of detection, using an inflated value of the probability of detection in PDA can actually lead to better performance.