Title :
Object kinematic model: A novel approach of adaptive background mixture models for video segmentation
Author :
Yu, Jin ; Zhou, Xuan ; Qian, Feng
Author_Institution :
Key Lab. of Adv. Control & Optimization for Chem. Processes, East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
In the field of video surveillance, adaptive Gaussian mixture model (GMM) is widely used as the background-pixel dynamic modeling approach. GMM produced each pixel Gaussian distribution corresponds to the respective, but this ignores the impact of the movement of the object itself. The ideas of object kinematic model is presented to guide the number of distribution in the process of iterative, which can speed up the process of clustering, and the results indicate that this method can improve the efficiency and stability of background modeling.
Keywords :
Gaussian distribution; image resolution; image segmentation; iterative methods; video surveillance; Gaussian distribution; adaptive background mixture models; object kinematic model; video segmentation; video surveillance; Adaptation model; Analytical models; Gaussian distribution; Hidden Markov models; Kinematics; Pixel; Trajectory; adaptive GMM; background modelling; background subtraction; object kinematic model;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
DOI :
10.1109/WCICA.2010.5554402