Efficient object tracking by condentional and cascaded image sensing

We introduce a robust multi-object tracking for abstract multi-dimensional feature vectors. The Condensation and the Wavelet Approximated Reduced Vector Machine (W-RVM) approach are joined to spend only as much as necessary effort for easy to discriminate regions (Condensation) and measurement locations (W-RVM) of the feature space, but most for regions and locations with high statistical likelihood to contain the object of interest. The new 3D Cascaded Condensation Tracking (CCT) yields more than 10 times faster tracking than state-of-art detection methods. We demonstrate HCI applications by high resolution face tracking within a large camera scene with an active dual camera system.

Research Highlights
► Accurate and efficient 3D Cascaded Condensation Tracking (3D CCT).
► We unify Wavelet Reduced Vector Machines and Condensation for efficient tracking.
► Contract computational effort to regions of the feature space likely to contain objects.
► Refining Condensation to multi-dimensional abstract features and to multi-objects.
► HCI applications applying CCT of faces with an active dual camera system.