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
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;
Conference_Titel :
Information Technology and Computer Science, 2009. ITCS 2009. International Conference on
Conference_Location :
Kiev
Print_ISBN :
978-0-7695-3688-0
DOI :
10.1109/ITCS.2009.276