Title:  Randomized Online Tensor Robust PCA

Committee: 

Dr. Anderson, Advisor   

Dr. Romberg, Chair

Dr. Davenport

Abstract: The objective of the proposed research is to develop a new randomized tensor-based online robust PCA algorithm that preserves the multi-dimensional structures of data. Online robust PCA algorithms are widely used in signal processing applications such as video surveillance, denoising, and anomaly detection. However, these methods are performed on data vectors and cannot directly be applied to higher-order data arrays. Our algorithm is based on the recently proposed tensor singular value decomposition (T-SVD). We consider the application of background/foreground separation in a video stream. The background component is modeled as a gradually changing low-rank subspace. The foreground component is modeled as a sparse signal with a tensor dictionary outside the subspace. Extensive experiments on real-world videos are presented.