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A Benchmark of Motion Detection Algorithms for Static Camera: Application on CDnet 2012 Dataset

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Advances in Computing Systems and Applications (CSA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 50))

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Abstract

The main aim behind this study relates to comparing a variety of motion detection methods for a mono static camera and it endeavors to identify the best method for different environments and complex backgrounds whether in indoor or outdoor scenes. For this reason, we used the CDnet 2012 video dataset as a benchmark that comprises numerous challenging problems, ranging from basic simple scenes to complex scenes affected by shadows and dynamic backgrounds. Eleven ranging from simple to complex motion detection methods are tested and several performance metrics are used to achieve a precise evaluation of the results. This study sets its objective to enable any user to identify the best method that suits his/her need.

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Abbreviations

\( I_{t} (x,y) \) :

Pixel coordinates \( (x,y) \) in the current image \( I_{t} \).

\( Th, \, T \) :

Threshold values.

\( U \) :

The energy function.

\( U_{m} \) :

Energy that ensures spatio-temporal homogeneity.

\( U_{a} \) :

The adequacy energy that ensures good coherence of the solution compared to the observed data.

\( c(s,r) \) :

Denotes a set of binary cliques associated with the chosen neighbourhood system.

\( B_{t} (x,y) \) :

Pixel coordinates \( (x,y) \) in the background image \( B_{t} \).

\( \alpha \), \( \rho \):

Learning rates.

\( \left( {\mu ,\sigma } \right) \) :

Mean and standard deviation of Gaussian distribution.

\( \omega_{k,t} \) :

The estimated weight associated with the kth Gaussian at time \( t \).

\( D \) :

Deviation threshold.

\( H_{x,y,q} \) :

Spatio-temporal histogram for each pixel.

\( P_{x,y,q} \) :

Probability density functions for each pixel.

\( E_{x,y} \) :

The entropy of the pixel \( (x,y) \).

\( \Phi \) :

Quantization function quantizes the 256 grey level values into Q grey levels.

\( ES \) :

Eigen-space formed using N reshaped frames.

\( C \) :

Covariance matrix.

\( V,V_{M} \) :

The eigenvector matrix. The M eigenvectors in \( V \) that correspond to the M largest eigenvalues.

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Author information

Authors and Affiliations

  1. TSS Laboratory, University Amar Telidji of Laghouat, Laghouat, Algeria

    Kamal Sehairi & Chouireb Fatima

  2. DIRO, University of Montreal, Montreal, Canada

    Jean Meunier

Authors
  1. Kamal Sehairi
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  2. Chouireb Fatima
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  3. Jean Meunier
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Corresponding author

Correspondence to Kamal Sehairi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Oualid Demigha

  2. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Badis Djamaa

  3. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Abdenour Amamra

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Sehairi, K., Fatima, C., Meunier, J. (2019). A Benchmark of Motion Detection Algorithms for Static Camera: Application on CDnet 2012 Dataset. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_25

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