OR-PCA with Dynamic Feature Selection for Robust Background Subtraction

Introduction

Online Robust PCA (OR-PCA) has the ability to process such large dimensional data via stochastic manners. OR-PCA processes one frame per time instance and updates the subspace basis accordingly when a new frame arrives. However, due to the lack of fea tures, the sparse component of OR-PCA is not always robust to handle various background modeling challenges. As a consequence, the system shows a very weak performance, which is not desirable for real applications. To handle these challenges, this paper presents a multi-feature based OR-PCA scheme. A multi-feature model is able to build a ro-bust low-rank background model of the scene. In addition, a very nice feature selection process is designed to dynamically select a useful set of features frame by frame, according to the weighted sum of total features.

Principle

Our scheme consists of several steps: multiple features extraction, feature background model, update feature model, OR-PCA, dynamic feature selection and foreground detection, which are shown in Figure 1.

Figure 1: Overview of Multiple feature based ORPCA-DFS

Publication

Chapter

S. Javed, S. Oh, T. Bouwmans, S. Jung, "Stochastic RPCA for Background/Foreground Separation", Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May 2016. [pdf]

Journal

S. Javed, S. Oh, T. Bouwmans, S. Jung, "Robust Background Subtraction to Global Illumination Changes via Multiple Features based OR-PCA with MRF", Journal of Electronic Imaging, 2015. [pdf]

Conference

S. Javed, S. Oh, A. Sobral, T. Bouwmans , S. Jung, "Background Subtraction via Superpixel-based Online Matrix Decomposition with Structured Foreground Constraints", Workshop on Robust Subspace Learning and Computer Vision, ICCV 2015, Santiago, Chile, December 2015.[pdf]

S. Javed, T. Bouwmans, S. Jung, “Combining ARF and OR-PCA Background Subtraction of Noisy Videos”, International Conference in Image Analysis and Applications, ICIAP 2015, Genova, Italy, September 2015. [pdf]

S. Javed, T. Bouwmans, S. Jung, “Stochastic Decomposition into Low Rank and Sparse Tensor for Robust Background Subtraction”, ICDP 2015, July 2015. [pdf]

S. Javed, T. Bouwmans, S. Jung, "Depth Extended Online RPCA with Spatiotemporal Constraints for Robust Background Subtraction", Korea-Japan Workshop on Frontiers of Computer Vision, FCV 2015, Mokpo, South Korea, January 2015. [pdf]

S. Javed, A. Sobral, T. Bouwmans, S. Jung, "OR-PCA with Dynamic Feature Selection for Robust Background Subtraction", ACM Symposium On Applied Computing, SAC 2015, Salamanca, Spain, April 2015. [pdf]

S. Javed, A. Sobral, S. Oh, T. Bouwmans, S. Jung, “OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic Backgrounds”, Asian Conference on Computer Vision, ACCV 2014, Singapore, November 2014. [pdf]