Quality-based fusion of multiple video sensors for video surveillance

Lauro Snidaro; Ruixin Niu; Gian Luca Foresti; Pramond K. Varshney

doi:10.1109/TSMCB.2007.895331

Quality-based fusion of multiple video sensors for video surveillance

Lauro Snidaro, Ruixin Niu, Gian Luca Foresti, Pramond K. Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

Original language	English (US)
Pages (from-to)	1044-1051
Number of pages	8
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	37
Issue number	4
DOIs	https://doi.org/10.1109/TSMCB.2007.895331
State	Published - Aug 2007

Keywords

Data fusion
Object tracking
Segmentation quality
Video surveillance

ASJC Scopus subject areas

Control and Systems Engineering
Software
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMCB.2007.895331

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title = "Quality-based fusion of multiple video sensors for video surveillance",

abstract = "In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.",

keywords = "Data fusion, Object tracking, Segmentation quality, Video surveillance",

author = "Lauro Snidaro and Ruixin Niu and Foresti, {Gian Luca} and Varshney, {Pramond K.}",

note = "Funding Information: Manuscript received April 13, 2006; revised September 26, 2006. This work was supported in part by the Italian Ministry of University and Scientific Research within the Project PRIN06 (Ambient Intelligence: Event analysis, sensor reconfiguration, and multimodal interfaces), in part by the European Project SEC6-SA-204400 (HAMLeT), and in part by the Army Research Office (ARO) under Grant W911NF-06-1-0250. This paper was recommended by Associate Editor Q. Zhu.",

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doi = "10.1109/TSMCB.2007.895331",

language = "English (US)",

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AU - Niu, Ruixin

AU - Foresti, Gian Luca

AU - Varshney, Pramond K.

N1 - Funding Information: Manuscript received April 13, 2006; revised September 26, 2006. This work was supported in part by the Italian Ministry of University and Scientific Research within the Project PRIN06 (Ambient Intelligence: Event analysis, sensor reconfiguration, and multimodal interfaces), in part by the European Project SEC6-SA-204400 (HAMLeT), and in part by the Army Research Office (ARO) under Grant W911NF-06-1-0250. This paper was recommended by Associate Editor Q. Zhu.

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N2 - In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

AB - In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

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KW - Segmentation quality

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Quality-based fusion of multiple video sensors for video surveillance

Abstract

Keywords

ASJC Scopus subject areas

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