Edge noise removal in multimodal background modeling techniques

J. W. Choi; S. Apewokin; B. E. Valentine; D. S. Wills; L. M. Wills

doi:10.1117/12.766829

26 February 2008 Edge noise removal in multimodal background modeling techniques

J. W. Choi, S. Apewokin, B. E. Valentine, D. S. Wills, L. M. Wills

Proceedings Volume 6813, Image Processing: Machine Vision Applications; 68130K (2008) https://doi.org/10.1117/12.766829
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Traditional video scene analysis depends on accurate background modeling techniques to segment objects of interest. Multimodal background models such as Mixture of Gaussian (MOG) and Multimodal Mean (MM) are capable of handling dynamic scene elements and incorporating new objects into the background. Due to the adaptive nature of these techniques, new pixels have to be observed consistently over time before they can be incorporated into the background. However, pixels in the boundary between two colors tend to fluctuate more, creating false positive pixels that result in less accurate foreground segmentation. To correct this, a simple and computationally efficient edge detection based algorithm is proposed. On average, approximately 70 percent of these false positives can be eliminated with little computational overhead.

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J. W. Choi, S. Apewokin, B. E. Valentine, D. S. Wills, and L. M. Wills "Edge noise removal in multimodal background modeling techniques", Proc. SPIE 6813, Image Processing: Machine Vision Applications, 68130K (26 February 2008); https://doi.org/10.1117/12.766829

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KEYWORDS

Edge detection

Image segmentation

Digital filtering

Detection and tracking algorithms

Linear filtering

Motion models

Light sources and illumination

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