Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map

Abstract:

Article Preview

The background modeling method based on the Gaussian mixture model (GMM) is usually used to detect the moving objects in static background. But when applied to dynamic background, for example caused by camera jitter, the wrong detection rate of moving objects is high, and thus affects the follow-up tracking. In addition, the method with GMM can not effectively remove the moving objects shadow region. This paper proposes a moving object detection method based on GMM and visual saliency maps, which not only can remove the disturbance caused by camera jitter, but also can effectively solve the shadow problem and achieve stable moving objects detection.

Info:

Periodical:

Edited by:

Helen Zhang and David Jin

Pages:

350-354

DOI:

10.4028/www.scientific.net/AMM.63-64.350

Citation:

L. L. Lin and N. R. Chen, "Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map", Applied Mechanics and Materials, Vols. 63-64, pp. 350-354, 2011

Online since:

June 2011

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Price:

$35.00

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