TY - GEN
T1 - Change detection for video sequences based on incremental subspace learning
AU - Portillo-Portillo, Jose
AU - Hernandez-Sanabria, Blas
AU - Perez-Meana, Hector
AU - Sanchez-Perez, Gabriel
AU - Toscano-Medina, Karina
AU - Olivares-Mercado, Jesus
AU - Nakano-Miyatake, Mariko
AU - Castro-Madrid, L. Carlos
AU - Sanchez-Silva, Victor
N1 - Publisher Copyright:
© 2018 The authors and IOS Press. All rights reserved.
PY - 2018
Y1 - 2018
N2 - This paper proposes a novel methodology for change detection in video sequences, which consists in the use of projection of the first eigenvector over the current frame in the video sequence. These eigenvectors are computed using the Incremental Principal Component Analysis (IPCA), assuming that the incremental computation of the eigenvalues and eigenvectors is made using the incremental block approach considering only two frames i.e. the past and the current frames in each incremental block. The main contribution of this work, is the use of the idea that the first eigenvector projects the maximum variability in their data matrix and then by using the incremental block of two frames in the IPCA, the maximum variability in those images could be considered as the change between them; such that after the post-processing in the projected matrix, we are able to labeled the change between the past and the current frames.
AB - This paper proposes a novel methodology for change detection in video sequences, which consists in the use of projection of the first eigenvector over the current frame in the video sequence. These eigenvectors are computed using the Incremental Principal Component Analysis (IPCA), assuming that the incremental computation of the eigenvalues and eigenvectors is made using the incremental block approach considering only two frames i.e. the past and the current frames in each incremental block. The main contribution of this work, is the use of the idea that the first eigenvector projects the maximum variability in their data matrix and then by using the incremental block of two frames in the IPCA, the maximum variability in those images could be considered as the change between them; such that after the post-processing in the projected matrix, we are able to labeled the change between the past and the current frames.
KW - Change detection
KW - IPCA
KW - Motion detection
KW - Subspace learning
UR - http://www.scopus.com/inward/record.url?scp=85063413035&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-900-3-71
DO - 10.3233/978-1-61499-900-3-71
M3 - Contribución a la conferencia
T3 - Frontiers in Artificial Intelligence and Applications
SP - 71
EP - 84
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 17th International Conference, SoMeT 2018
A2 - Fujita, Hamido
A2 - Herrera-Viedma, Enrique
PB - IOS Press BV
T2 - 17th International Conference on New Trends in Intelligent Software Methodology Tools and Techniques, SoMeT 2018
Y2 - 26 September 2018 through 28 September 2018
ER -