Efficient Data Association in Visual Sensor Networks with Missing Detection

Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian n...
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Gespeichert in:

Autor*in:	Wan, Jiuqing [verfasserIn] Liu, Qingyun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Conditional Independence Belief State Inference Algorithm Inference Problem Dynamic Bayesian Network

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2011(2011), 1 vom: 13. März
Übergeordnetes Werk:	volume:2011 ; year:2011 ; number:1 ; day:13 ; month:03

Links:	Volltext

DOI / URN:	10.1155/2011/176026

Katalog-ID:	SPR031997295

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spelling	10.1155/2011/176026 doi (DE-627)SPR031997295 (SPR)176026-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Wan, Jiuqing verfasserin aut Efficient Data Association in Visual Sensor Networks with Missing Detection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods. Conditional Independence (dpeaa)DE-He213 Belief State (dpeaa)DE-He213 Inference Algorithm (dpeaa)DE-He213 Inference Problem (dpeaa)DE-He213 Dynamic Bayesian Network (dpeaa)DE-He213 Liu, Qingyun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:13 month:03 https://dx.doi.org/10.1155/2011/176026 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 13 03
allfields_unstemmed	10.1155/2011/176026 doi (DE-627)SPR031997295 (SPR)176026-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Wan, Jiuqing verfasserin aut Efficient Data Association in Visual Sensor Networks with Missing Detection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods. Conditional Independence (dpeaa)DE-He213 Belief State (dpeaa)DE-He213 Inference Algorithm (dpeaa)DE-He213 Inference Problem (dpeaa)DE-He213 Dynamic Bayesian Network (dpeaa)DE-He213 Liu, Qingyun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:13 month:03 https://dx.doi.org/10.1155/2011/176026 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 13 03
allfieldsGer	10.1155/2011/176026 doi (DE-627)SPR031997295 (SPR)176026-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Wan, Jiuqing verfasserin aut Efficient Data Association in Visual Sensor Networks with Missing Detection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods. Conditional Independence (dpeaa)DE-He213 Belief State (dpeaa)DE-He213 Inference Algorithm (dpeaa)DE-He213 Inference Problem (dpeaa)DE-He213 Dynamic Bayesian Network (dpeaa)DE-He213 Liu, Qingyun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:13 month:03 https://dx.doi.org/10.1155/2011/176026 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 13 03
allfieldsSound	10.1155/2011/176026 doi (DE-627)SPR031997295 (SPR)176026-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Wan, Jiuqing verfasserin aut Efficient Data Association in Visual Sensor Networks with Missing Detection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods. Conditional Independence (dpeaa)DE-He213 Belief State (dpeaa)DE-He213 Inference Algorithm (dpeaa)DE-He213 Inference Problem (dpeaa)DE-He213 Dynamic Bayesian Network (dpeaa)DE-He213 Liu, Qingyun verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2011(2011), 1 vom: 13. März (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2011 year:2011 number:1 day:13 month:03 https://dx.doi.org/10.1155/2011/176026 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2011 2011 1 13 03
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2011(2011), 1 vom: 13. März volume:2011 year:2011 number:1 day:13 month:03
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author	Wan, Jiuqing
spellingShingle	Wan, Jiuqing ddc 620 bkl 53.73 misc Conditional Independence misc Belief State misc Inference Algorithm misc Inference Problem misc Dynamic Bayesian Network Efficient Data Association in Visual Sensor Networks with Missing Detection
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topic_title	620 ASE 53.73 bkl Efficient Data Association in Visual Sensor Networks with Missing Detection Conditional Independence (dpeaa)DE-He213 Belief State (dpeaa)DE-He213 Inference Algorithm (dpeaa)DE-He213 Inference Problem (dpeaa)DE-He213 Dynamic Bayesian Network (dpeaa)DE-He213
topic	ddc 620 bkl 53.73 misc Conditional Independence misc Belief State misc Inference Algorithm misc Inference Problem misc Dynamic Bayesian Network
topic_unstemmed	ddc 620 bkl 53.73 misc Conditional Independence misc Belief State misc Inference Algorithm misc Inference Problem misc Dynamic Bayesian Network
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title	Efficient Data Association in Visual Sensor Networks with Missing Detection
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title_full	Efficient Data Association in Visual Sensor Networks with Missing Detection
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title_sort	efficient data association in visual sensor networks with missing detection
title_auth	Efficient Data Association in Visual Sensor Networks with Missing Detection
abstract	Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods.
abstractGer	Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods.
abstract_unstemmed	Abstract One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods.
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title_short	Efficient Data Association in Visual Sensor Networks with Missing Detection
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