Open access
Date
2012Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Multi sensor fusion has been widely used in recognition problems. Most existing works highly depend on the calibration between different sensors, but less on modeling and reasoning of the co-incidence of multiple hints. In this paper, we propose a generic framework for recognition and clustering problem using a non-parametric Dirichlet hierarchical model, named DP-Fusion. It enables online labeling, clustering and recognition of sequential data simultaneously, while considering multiple types of sensor readings. The algorithm is data-driven, which does not depend on priorknowledge of the data structure. The results show the feasibility and reliability against noise data. Show more
Permanent link
https://doi.org/10.3929/ethz-a-010034783Publication status
publishedExternal links
Book title
2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
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ETH Bibliography
yes
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