Gaussian mixture background model with shadow information

Jung Ming Wang; Sei Wang Chen; Chiou Shann Fuh

Gaussian mixture background model with shadow information

Jung Ming Wang^*, Sei Wang Chen, Chiou Shann Fuh

^*Corresponding author for this work

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model.

Original language	English
Title of host publication	Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011
Pages	217-222
Number of pages	6
Publication status	Published - 2011
Event	IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 - Rome, Italy Duration: 2011 Jul 24 → 2011 Jul 26

Publication series

Name	Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011

Conference

Conference	IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
Country/Territory	Italy
City	Rome
Period	2011/07/24 → 2011/07/26

Keywords

Adaptive Gaussian mixture model
Dynamic scene
Foreground detection
Shadow detection

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Information Systems

Cite this

Wang, J. M., Chen, S. W., & Fuh, C. S. (2011). Gaussian mixture background model with shadow information. In Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011 (pp. 217-222). (Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011).

Gaussian mixture background model with shadow information. / Wang, Jung Ming; Chen, Sei Wang; Fuh, Chiou Shann.
Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. p. 217-222 (Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, JM, Chen, SW & Fuh, CS 2011, Gaussian mixture background model with shadow information. in Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011. Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011, pp. 217-222, IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011, Rome, Italy, 2011/07/24.

Wang JM, Chen SW, Fuh CS. Gaussian mixture background model with shadow information. In Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. p. 217-222. (Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011).

Wang, Jung Ming ; Chen, Sei Wang ; Fuh, Chiou Shann. / Gaussian mixture background model with shadow information. Proc. of the IADIS Int. Conf, Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011. 2011. pp. 217-222 (Proc. of the IADIS Int. Conf. Computer Graphics, Visualization, Computer Vision and Image Processing 2011, Part of the IADIS Multi Conf. on Computer Science and Information Systems 2011, MCCSIS 2011).

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title = "Gaussian mixture background model with shadow information",

abstract = "In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model.",

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author = "Wang, {Jung Ming} and Chen, {Sei Wang} and Fuh, {Chiou Shann}",

year = "2011",

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TY - GEN

T1 - Gaussian mixture background model with shadow information

AU - Wang, Jung Ming

AU - Chen, Sei Wang

AU - Fuh, Chiou Shann

PY - 2011

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N2 - In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model.

AB - In this paper, we integrate shadow information into the background model of a scene in an attempt to detect both shadows and foreground objects at a time. Since shadows accompanying foreground objects are viewed as parts of the foreground objects, shadows will be extracted as well during foreground object detection. Shadows can distort object shapes and may connect multiple objects into one object. On the other hand, shadows tell the directions of light sources. In other words, shadows can be advantageous as well as disadvantageous. To begin, we use an adaptive Gaussian mixture model to describe the background of a scene. Based on this preliminary background model, we extract foreground objects and their accompanying shadows. Shadows are next separated from foreground objects through a series of intensity and color analyses. The characteristics of shadows are finally determined with the principal component analysis method and are embedded as an additional Gaussian in the background model. Experimental results demonstrated the feasibility of the proposed background model.

KW - Adaptive Gaussian mixture model

KW - Dynamic scene

KW - Foreground detection

KW - Shadow detection

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Y2 - 24 July 2011 through 26 July 2011

ER -

Gaussian mixture background model with shadow information

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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