Abstract
Background subtraction is a fundamental video analysis technique that consists of creation of a background model that allows distinguishing foreground pixels. We present a new method in which the image sequence is assumed to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix. The decomposition task is then solved using our approximated Robust Principal Component Analysis (ARPCA) method which is an extension to the RPCA that can handle camera motion and noise. Our model dynamically estimates the support of the foreground regions via a superpixel generation step, so that spatial coherence can be imposed on these regions. Unlike conventional smoothness constraints such as MRF, our method is able to obtain crisp and meaningful foreground regions, and in general, handles large dynamic background motion better. To reduce the dimensionality and the curse of scale that is persistent in the RPCA-based methods, we model the background via Column Subset Selection Problem, that reduces the order of complexity and hence decreases computation time. Comprehensive evaluation on four benchmark datasets demonstrate the effectiveness of our method in outperforming state-of-the-art alternatives.
Highlights
Background subtraction can be defined as segmentation of a video sequence into the foreground and the background
To overcome the inherent limitations of RPCA for background subtraction and foreground detection, we propose to an approximated form of the Robust Principal Component
We have presented a new background subtraction method and validated its efficacy and effectiveness with extensive testing
Summary
Background subtraction can be defined as segmentation of a video sequence into the foreground and the background. The dynamicity of group structures is either controlled via a patch-based group selection algorithm, or derived from the natural shape of objects in the scene – by selecting clusters of pixels via the SLIC superpixels [2], and dynamically refining the size of these clusters in an iterative process This is effective in reducing the foreground aperture problem with rigorous experimental evaluations. In a nutshell contributions of this paper are: low-rank approximation of the background to accommodate small scene and illumination changes to some extent; inducing structuredsparsity in a novel group structure, namely a dynamic block structure and a dynamic superpixel structure; insensitivity to foreground object size, as a result of using within-patch normalization; assumption of a noise part in decomposition for reducing false positive pixels (false alarms); and a dimensionality reduction for RPCA problem via the Column Subset Selection Problem that alleviates bootstrapping, and reduces computational complexity and cost, and an analysis of the efficacy of this method. An exhaustive evaluation using four datasets [4], [5], [6], [7], demonstrating top performance in comparison with the state-of-the-art alternatives is presented
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