Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding
published: Feb. 25, 2007, recorded: June 2005, views: 7604
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This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman —lter (PKF), allowing the problem to be treated in a single, uni—ed framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the e®ectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.
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