Studies in numerical linear algebra
Studies in numerical linear algebra
Recursive estimation of motion parameters
Computer Vision and Image Understanding
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Journal of Cognitive Neuroscience
Illumination-robust variational optical flow with photometric invariants
Proceedings of the 29th DAGM conference on Pattern recognition
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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In different tasks such as adaptive background modelling, the Singular Value Decomposition (SVD) has to be applied in running fashion. Typically, this happens when the SVD is used in a sliding spatial or temporal data window. Each time the window moves on, the SVD should be calculated in the batch mode from scratch, or re-calculated using the previous solution. When the data matrix is relatively small, the batch mode is fast enough. When the matrix is large, the batch mode is prohibitive and fast re-calculation is needed. In background modelling for video surveillance, the data matrix is formed by consecutive frames and can be fairly large. The existing PCA or SVD based approaches use approximate solutions and divide the frame into small blocks to speed up the re-calculation. We present a fast approximation-free solution for running SVD and apply it to moving object detection in video. Photometric invariants are used for dynamic background with frequent shading. The proposed approach is compared on challenging sequences to the Multiple Gaussian Method which is a standard in adaptive background modelling.