1 research outputs found
An advanced Bayesian model for the visual tracking of multiple interacting objects
Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable
algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in
uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel
Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished
by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend
on the inference of potential events of object occlusion. The proposed tracking model can also handle false and
missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other
hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories,
which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results
have been obtained using a publicly available database, proving the efficiency of the proposed approach