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Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding
This video was recorded at 2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms, Edinburgh 2005. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
