Reconstructive Subspace Learning
Background modeling is often used to detect moving object in video acquired by a fixed camera. Recently, subspace learning methods have been used to model the background in the idea to represent online data content while reducing dimension significantly. The first method using Principal Component Analysis (PCA) was proposed by Oliver et al. [1] and a representative patent using PCA concerns the detection of cars and persons in video surveillance [2]. Numerous improvements and variants were developed over the recent years. The purpose of this paper is to provide a survey and an original classification of these improvements. Firstly, we classify the improvements of the PCA in term of strategies and the variants in term of the used subspace learning algorithms. Then, we present a comparative evaluation of the variants and evaluate them with the state-of-art algorithms (SG, MOG, and KDE) by using the Wallflower dataset. (more information)
T. Bouwmans, “Subspace Learning for Background Modeling: A Survey”, Recent Patents on Computer Science, Volume 2, No 3, pages 223-234, November 2009.
Discriminative Subspace Learning
Subspace learning methods are widely used in background modeling to be robust to illumination changes.Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach. (more information)
D. Farcas, C. Marghes, T. Bouwmans, “Background Subtraction via Incremental Maximum Margin Criterion: A discriminative approach” , Machine Vision and Applications, March 2012.
C. Marghes, T. Bouwmans, "Background Modeling via Incremental Maximum Margin Criterion", International Workshop on Subspace Methods, ACCV 2010 Workshop Subspace 2010, Queenstown, New Zealand, November 2010.
D. Farcas, T. Bouwmans, "Background Modeling via a Supervised Subspace Learning", International Conference on Image, Video Processing and Computer Vision, IVPCV 2010, pages 1-7, Orlando, USA , July 2010.
Mixed Subspace Learning
Background subtraction is one of the basic low-level operations in video analysis. The aim is to separate static information called ”background” from the moving objects called ”foreground”. The background needs to be modeled and updated over time to allow robust foreground detection. Reconstructive subspace learning such as Principal Component Analysis (PCA) has been widely used in background modeling by significantly reducing the data’s dimension. However, reconstructive representations strive to be as informative as possible in terms of well approximating the original data. On the other hand, discriminative methods such as Linear Discriminant Analysis (LDA) provides a supervised reconstruction of the data which will often give better classification results when compared to the reconstructive methods. In this paper, we offer to use and validate the combination of a reconstructive method with a discriminative one to model robustly the background. The objective is firstly to enable a robust model of the background and secondly a robust classification of pixels as background or foreground. Results on different datasets demonstrate the performance of the proposed approach. (more information)
C. Marghes, T. Bouwmans, R. Vasiu, “Background Modeling and Foreground Detection via a Reconstructive and Discriminative Subspace Learning Approach”, International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012, Las Vegas, USA, July 2012.