A GMM-Based Segmentation Method for the Detection of Water Surface Floats

Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water sur...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.119018-119025
Main Authors: Jin, Xianli, Niu, Peiwen, Liu, Linfeng
Format: Article
Language:English
Subjects:
Algorithms
Background subtraction
Discriminant analysis
Floats
Gaussian mixture model
graph cuts
Image segmentation
light-shadow discriminant function
Moving object recognition
Probabilistic models
Shadows
Spatial data
Subtraction
video segmentation
water surface floats
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Summary:Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water surface floats effectively. To solve this problem, an improved GMM-based automatic segmentation method (IGASM) is proposed to detect the water surface floats in this paper, where the background updating strategy is improved to segment the water surface floats more effectively. Besides, the GMM results are mapped into an HSV color space, and a light-shadow discriminant function is applied to solve the problems of light and shadow. Then, a morphological method is used to smooth the extracted foregrounds. Finally, Graph Cuts algorithm is applied to optimized the segmentation results according to the spatial information of video images. Experimental results demonstrate that IGASM can detect the water surface floats quickly and accurately, and the influences of light, shadows and ripples of water surface can be eliminated as much as possible.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2937129