A Bayesian Network for online evaluation of sparse features based multitarget tracking

Online evaluation of tracking algorithms has received attentions in computer vision community to detect failures and apply correction methods for achieving better performances. In this paper, a novel online evaluation framework is proposed for a multitarget feature points based object tracking. An online partial least square regression and correlation model is constructed from short trajectory histories for the tracks. The model allows to estimate the state of one track from the other track states. The core idea for the method is creating a virtual reference data for evaluation from the learned model. The proposed self-evaluation mechanism is presented as a Dynamic Bayesian Network. The method is evaluated on a simulation data for tracking feature points from a pedestrian.