Abstract
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 filter (PKF), allowing the problem to be treated in a single, unified 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 effectiveness 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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Canton-Ferrer, C., Casas, J.R., Tekalp, A.M., Pardàs, M. (2006). Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_22
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DOI: https://doi.org/10.1007/11677482_22
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