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Sensor and Data Fusion: Taxonomy, Challenges and Applications

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

Published

Standard

Sensor and Data Fusion: Taxonomy, Challenges and Applications. / Klein, Lawrence; Mihaylova, Lyudmila; El Faouzi, Nour-Eddin.
Handbook on Soft Computing for Video Surveillance. ed. / S.K. Pal; A. Petrosino; L. Maddalena. USA: Chapman & Hall, 2012. p. 139-183.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed)

Harvard

Klein, L, Mihaylova, L & El Faouzi, N-E 2012, Sensor and Data Fusion: Taxonomy, Challenges and Applications. in SK Pal, A Petrosino & L Maddalena (eds), Handbook on Soft Computing for Video Surveillance. Chapman & Hall, USA, pp. 139-183. <http://www.crcpress.com/product/isbn/9781439856840;jsessionid=d9bWGJGPl5Bx+-ja4NOSIw**>

APA

Klein, L., Mihaylova, L., & El Faouzi, N-E. (2012). Sensor and Data Fusion: Taxonomy, Challenges and Applications. In S. K. Pal, A. Petrosino, & L. Maddalena (Eds.), Handbook on Soft Computing for Video Surveillance (pp. 139-183). Chapman & Hall. http://www.crcpress.com/product/isbn/9781439856840;jsessionid=d9bWGJGPl5Bx+-ja4NOSIw**

Vancouver

Klein L, Mihaylova L, El Faouzi N-E. Sensor and Data Fusion: Taxonomy, Challenges and Applications. In Pal SK, Petrosino A, Maddalena L, editors, Handbook on Soft Computing for Video Surveillance. USA: Chapman & Hall. 2012. p. 139-183

Author

Klein, Lawrence ; Mihaylova, Lyudmila ; El Faouzi, Nour-Eddin. / Sensor and Data Fusion : Taxonomy, Challenges and Applications. Handbook on Soft Computing for Video Surveillance. editor / S.K. Pal ; A. Petrosino ; L. Maddalena. USA : Chapman & Hall, 2012. pp. 139-183

Bibtex

@inbook{cffa69f69947491a844bc6c06082a78c,
title = "Sensor and Data Fusion: Taxonomy, Challenges and Applications",
abstract = "Sensor and data fusion is a process of paramount importance for many domains and applications. Its potential for rapid data and information processing are of primary importance for surveillance, security, intelligent transportation systems, navigation and communications. Effective use of the data requires the sensor and context data to be aggregated or “fused” in such a way that high quality information results and serves as a basis for decision support. Data fusion encompasses groups of methods for merging various types of data and information. This process is especially important for tracking systems. This chapter presents taxonomy of sensor data fusion methods. Applications from object tracking in video are presented.",
keywords = "sensor data fusion , Taxonomy, tracking, Bayesian inference, video",
author = "Lawrence Klein and Lyudmila Mihaylova and {El Faouzi}, Nour-Eddin",
year = "2012",
month = jan,
language = "English",
isbn = "978-1439856840",
pages = "139--183",
editor = "S.K. Pal and { Petrosino}, A. and L. Maddalena",
booktitle = "Handbook on Soft Computing for Video Surveillance",
publisher = "Chapman & Hall",

}

RIS

TY - CHAP

T1 - Sensor and Data Fusion

T2 - Taxonomy, Challenges and Applications

AU - Klein, Lawrence

AU - Mihaylova, Lyudmila

AU - El Faouzi, Nour-Eddin

PY - 2012/1

Y1 - 2012/1

N2 - Sensor and data fusion is a process of paramount importance for many domains and applications. Its potential for rapid data and information processing are of primary importance for surveillance, security, intelligent transportation systems, navigation and communications. Effective use of the data requires the sensor and context data to be aggregated or “fused” in such a way that high quality information results and serves as a basis for decision support. Data fusion encompasses groups of methods for merging various types of data and information. This process is especially important for tracking systems. This chapter presents taxonomy of sensor data fusion methods. Applications from object tracking in video are presented.

AB - Sensor and data fusion is a process of paramount importance for many domains and applications. Its potential for rapid data and information processing are of primary importance for surveillance, security, intelligent transportation systems, navigation and communications. Effective use of the data requires the sensor and context data to be aggregated or “fused” in such a way that high quality information results and serves as a basis for decision support. Data fusion encompasses groups of methods for merging various types of data and information. This process is especially important for tracking systems. This chapter presents taxonomy of sensor data fusion methods. Applications from object tracking in video are presented.

KW - sensor data fusion

KW - Taxonomy

KW - tracking

KW - Bayesian inference

KW - video

M3 - Chapter (peer-reviewed)

SN - 978-1439856840

SP - 139

EP - 183

BT - Handbook on Soft Computing for Video Surveillance

A2 - Pal, S.K.

A2 - Petrosino, A.

A2 - Maddalena, L.

PB - Chapman & Hall

CY - USA

ER -