NDLI: Mixture of Merged Gaussian Algorithm using RTDENN

Content Provider	Springer Nature Link
Author	Alvar, Manuel Rodriguez Calvo, Andrea Sanchez Miralles, Alvaro Arranz, Alvaro
Copyright Year	2013
Abstract	Computer vision has been a widely developed research area in the last years, and it has been used for a broad range of applications, including surveillance systems. In the pursuit of an autonomous and smart motion detection system, a reliable segmentation algorithm is required. The main problems of present segmentation solutions are their high execution time and the lack of robustness against changes in the environment due to variations in lighting, shadows, occlusions or the movement of secondary objects. This paper proposes a new algorithm named Mixture of Merged Gaussian Algorithm (MMGA) that aims to achieve a substantial improvement in execution speed to enable real-time implementation, without compromising the reliability and accuracy of the segmentation. The MMGA is based on the combination of a probabilistic model for the background, similar to the Mixture of Gaussian Model (MGM), with the learning processes of Real-Time Dynamic Ellipsoidal Neural Networks (RTDENN) for the update of the model. The proposed algorithm has been tested for different videos and compared to the MGM and SDGM algorithms. Results show a reduction of 30 to 50 % in execution times. Furthermore, the segmentation is more robust against the effect of noise and adapts faster to lighting changes.
Starting Page	1133
Ending Page	1144
Page Count	12
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	25
Issue Number	5
e-ISSN	14321769
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-10-12
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computer vision Video surveillance Object detection Image motion analysis Object segmentation Motion detection Mixture of Gaussian model Real-Time Dynamic Ellipsoidal neural network Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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