Open AccessTwo‐dimension principal component analysis‐based motion detection framework with subspace update of background
Author(s) -
Zhou Zongwei,
Jin Zhong
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0298
Subject(s) - background subtraction , principal component analysis , subspace topology , artificial intelligence , computer science , pattern recognition (psychology) , object detection , foreground detection , computer vision , dimension (graph theory) , pixel , motion detection , motion (physics) , dimensionality reduction , mathematics , pure mathematics
Object detection plays a critical role for automatic video analysis in many vision applications. Background subtraction has been the mainstream in the field of moving objects detection. However, most of state‐of‐the‐art techniques of background subtraction operate on each pixel independently ignoring the global features of images. A motion detection method based on subspace update of background is proposed in this study. This method uses a subspace spanned by the principal components of background sequence to characterise the background and integrates the regional continuity of objects to segment the foreground. To deal with changes in the background geometry, a learning factor is introduced into the authors’ model to update the subspace timely. Additionally, to reduce computational complexity, they use two‐dimension principal component analysis (PCA) rather than traditional PCA to obtain the principal components of background. Experiments demonstrate that the update policy is effective and in most cases this proposed method can achieve better results than others compared in this study.