Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Abstract

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.

To appear in IEEE TIP

References in corpus (6)

• Fast and Efficient Compressive Sensing using Structurally Random Matrices
• D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization
• An Online Algorithm for Separating Sparse and Low-dimensional Signal Sequences from their Sum
• PETRELS: Parallel Subspace Estimation and Tracking by Recursive Least Squares from Partial Observations
• Compressive Sensing of Sparse Tensors
• Surveillance Video Processing Using Compressive Sensing

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• Comparative study of motion detection methods for video surveillance systems
• Tensor Recovery Based on A Novel Non-convex Function Minimax Logarithmic Concave Penalty Function
• Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
• A Unified Algorithmic Framework for Dynamic Compressive Sensing