Efficient object tracking by condentional and cascaded image sensing

Authors:
Matthias RäTsch;Clemens Blumer;Thomas Vetter;Gerd Teschke
Affiliations:
University of Basel, Bernoullistrasse 16, CH-4056 Basel, Switzerland;University of Basel, Bernoullistrasse 16, CH-4056 Basel, Switzerland;University of Basel, Bernoullistrasse 16, CH-4056 Basel, Switzerland;University of Applied Sciences Neubrandenburg, Brodaer Str. 2., D-17033 Neubrandenburg, Germany
Venue:
Computer Standards & Interfaces
Year:
2012

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Abstract

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.