Change Detection for Video Sequences Based on Incremental Subspace Learning
José Portillo-Portillo, Blas Hernandez-Sanabria, Hector Manuel Perez-Meana, Gabriel Sánchez-Pérez, Karina Toscano-Medina, Jesús Olivares-Mercado, Mariko Nakano-Miyatake, Castro-Madrid L. Carlos, Victor Sanchez-Silva · Frontiers in artificial intelligence and applications · 2018
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.