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  4. An adaptive wavelet shrinkage based accumulative frame differencing model for motion segmentation

An adaptive wavelet shrinkage based accumulative frame differencing model for motion segmentation

MMC.
2023;
Volume 10, Number 1
: pp. 159–170
https://doi.org/10.23939/mmc2023.01.159
Received: May 30, 2022
Accepted: November 09, 2022

Mathematical Modeling and Computing, Vol. 10, No. 1, pp. 159–170 (2023)

Authors:
  1. M. J. Lahgazi
  2. A. Hakim
  3. P. Argoul
1
Faculty of Sciences and Technics, Cadi Ayyad University, Marrakesh, Morocco
2
Faculty of Sciences and Technics, Cadi Ayyad University, Marrakesh, Morocco
3
MAST-EMGCU, Université Gustave Eiffel, IFSTTAR, F-77477 Marne-la-Vallée, France

Motion segmentation in real-world scenes is a fundamental component in computer vision.  There exists a variety of motion recognition algorithms, each with varying degrees of accuracy and computational complexity.  The most widely used techniques, in the case of static cameras, are those based on frame difference.  Those methods have a significant weakness when it comes to detect slow moving objects.  Therefore, we introduce in this paper a novel approach that aims to improve motion segmentation by proposing an accumulative wavelet based frame differencing technique.  Moreover, in the proposed approach we exploit a combination of several techniques to efficiently enhance the quality of motion segmentation results.  The approach's performance on real-world video sequences shows that comparing frames using the 2D wavelet transform increases motion segmentation quality.

motion segmentation
wavelet transform
wavelet shrinkage
adaptive threshold
frame differencing
background subtraction
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