DocumentCode
496148
Title
A Robust Moving Objects Detection Algorithm Based on Gaussian Mixture Model
Author
Xuehua, Song ; Yu, Chen ; Jianfeng, Geng ; Jingzhu, Chen
Author_Institution
Dept. of Comput. Sci. & Telecommun. Eng., Jiangsu Univ., Zhenjiang, China
Volume
1
fYear
2009
fDate
25-26 July 2009
Firstpage
566
Lastpage
569
Abstract
The paper proposes a novel algorithm which can effectively resolve the problems of background disturbance and light changes in allusion to the problem that the background subtraction is sensitive to light changes. The algorithm, combined with the methods of background subtraction and adjacent frame difference, adopts Gaussian mixture model to avoid the impact of background disturbance. By using the idea of adjacent frame difference for reference, it deals with light changes by background reconstruction and constructing the function of dynamic learning efficiency. The algorithm is simulated under the circumstance of background disturbance and light changes, the experimental results show that the algorithm is more efficient and robust than traditional methods, and it can attain background model in the complex condition quickly. The algorithm is particularly suitable to the intelligent video surveillance with static cameras.
Keywords
Gaussian processes; image motion analysis; image reconstruction; object detection; video surveillance; Gaussian mixture model; adjacent frame difference; background disturbance; background reconstruction; background subtraction; dynamic learning efficiency function; intelligent video surveillance; robust moving objects detection algorithm; Cameras; Computer science; Equations; Gaussian distribution; Image analysis; Image reconstruction; Image segmentation; Object detection; Robustness; Video surveillance; Gaussian Mixture Model; Moving objects detection; Objects Detection Algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technology and Computer Science, 2009. ITCS 2009. International Conference on
Conference_Location
Kiev
Print_ISBN
978-0-7695-3688-0
Type
conf
DOI
10.1109/ITCS.2009.276
Filename
5190137
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