Publication: Projective Kalman Filter: multiocular tracking of 3D locations towards scene understanding
Program
KU-Authors
KU Authors
Co-Authors
Canton Ferrer, C.
Casas, J. R.
Pardas, M.
Publication Date
Language
Type
Embargo Status
Journal Title
Journal ISSN
Volume Title
Alternative Title
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.
Source
Publisher
Springer-Verlag Berlin
Subject
Computer Science, Artificial intelligence
Citation
Has Part
Source
Machine Learning for Multimodal Interaction
Book Series Title
Edition
DOI
10.1007/11677482_22