@conference {cCanton-Ferrer05b,
	title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding},
	booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms},
	year = {2005},
	abstract = {<p>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 {\textasciimacron}lter (PKF), allowing the problem to be treated in a single, uni{\textasciimacron}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{\textregistered}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.</p>},
	author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s}
}
