Real-Time Tracking of Multiple Occluding Objects Using Level Sets

Abstract

We derive a probabilistic framework for robust, realtime, visual tracking of multiple previously unseen objects from a moving camera. This framework models the discrete depth ordering of the objects being tracked in the scene. The method uses the observed image data to compute a posterior over the objects' poses, shapes and relative depths. The poses are group transformations, the shapes are implicit contours represented using level-sets and the relative depths give the discrete depth ordering of the objects. All nuisance variables are marginalised out at the pixel-level resulting in a pixel-wise posterior, as opposed to a pixel-wise likelihood, and we show using quantitative results that this provides increased resilience to noise. We also demonstrate how motion models can be incorporated within the same probabilistic framework and show how this enables the system to track complete occlusions. The effectiveness of our method is demonstrated on a variety of challenging video sequences.