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Mixture of Merged Gaussian Algorithm using RTDENN

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Machine Vision and Applications Aims and scope Submit manuscript

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.

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Notes

  1. http://www.iit.upcomillas.es/malvar/MMGA.php.

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Authors and Affiliations

  1. Institute for Research in Technology, Comillas Pontifical University, Alberto Aguilera 23, Madrid, 28015, Spain

    Manuel Alvar, Andrea Rodriguez-Calvo, Alvaro Sanchez-Miralles & Alvaro Arranz

Authors
  1. Manuel Alvar
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  2. Andrea Rodriguez-Calvo
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  3. Alvaro Sanchez-Miralles
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  4. Alvaro Arranz
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Correspondence to Manuel Alvar.

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Alvar, M., Rodriguez-Calvo, A., Sanchez-Miralles, A. et al. Mixture of Merged Gaussian Algorithm using RTDENN. Machine Vision and Applications 25, 1133–1144 (2014). https://doi.org/10.1007/s00138-013-0550-9

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  • DOI: https://doi.org/10.1007/s00138-013-0550-9

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