Information fusion for object & situation assessment in sensor networks

Abhishek Srivastav, Yicheng Wen, Evan Hendrick, Ishanu Chattopadhyay, Asok Ray, Shashi Phoha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

A semantic framework for information fusion in sensor networks for object and situation assessment is proposed. The overall vision is to construct machine representations that would enable human-like perceptual understanding of observed scenes via fusion of heterogeneous sensor data. In this regard, a hierarchical framework is proposed that is based on the Data Fusion Information Group (DFIG) model. Unlike a simple set-theoretic information fusion methodology that leads to loss of information, relational dependencies are modeled as cross-machines called relational Probabilistic Finite State Automata using the xD-Markov machine construction. This leads to a tractable approach for modeling composite patterns as structured sets for both object and scene representation. An illustrative example demonstrates the superior capability of the proposed methodology for pattern classification in urban scenarios.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages1274-1279
Number of pages6
StatePublished - Sep 29 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

Fingerprint

Information fusion
Sensor networks
Data fusion
Finite automata
Pattern recognition
Semantics
Sensors
Composite materials

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Srivastav, A., Wen, Y., Hendrick, E., Chattopadhyay, I., Ray, A., & Phoha, S. (2011). Information fusion for object & situation assessment in sensor networks. In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 1274-1279). [5991171]
Srivastav, Abhishek ; Wen, Yicheng ; Hendrick, Evan ; Chattopadhyay, Ishanu ; Ray, Asok ; Phoha, Shashi. / Information fusion for object & situation assessment in sensor networks. Proceedings of the 2011 American Control Conference, ACC 2011. 2011. pp. 1274-1279
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Srivastav, A, Wen, Y, Hendrick, E, Chattopadhyay, I, Ray, A & Phoha, S 2011, Information fusion for object & situation assessment in sensor networks. in Proceedings of the 2011 American Control Conference, ACC 2011., 5991171, pp. 1274-1279, 2011 American Control Conference, ACC 2011, San Francisco, CA, United States, 6/29/11.

Information fusion for object & situation assessment in sensor networks. / Srivastav, Abhishek; Wen, Yicheng; Hendrick, Evan; Chattopadhyay, Ishanu; Ray, Asok; Phoha, Shashi.

Proceedings of the 2011 American Control Conference, ACC 2011. 2011. p. 1274-1279 5991171.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - A semantic framework for information fusion in sensor networks for object and situation assessment is proposed. The overall vision is to construct machine representations that would enable human-like perceptual understanding of observed scenes via fusion of heterogeneous sensor data. In this regard, a hierarchical framework is proposed that is based on the Data Fusion Information Group (DFIG) model. Unlike a simple set-theoretic information fusion methodology that leads to loss of information, relational dependencies are modeled as cross-machines called relational Probabilistic Finite State Automata using the xD-Markov machine construction. This leads to a tractable approach for modeling composite patterns as structured sets for both object and scene representation. An illustrative example demonstrates the superior capability of the proposed methodology for pattern classification in urban scenarios.

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Srivastav A, Wen Y, Hendrick E, Chattopadhyay I, Ray A, Phoha S. Information fusion for object & situation assessment in sensor networks. In Proceedings of the 2011 American Control Conference, ACC 2011. 2011. p. 1274-1279. 5991171