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      Background-Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering.

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          Abstract

          Background estimation and foreground segmentation are important steps in many high-level vision tasks. Many existing methods estimate background as a low-rank component and foreground as a sparse matrix without incorporating the structural information. Therefore, these algorithms exhibit degraded performance in the presence of dynamic backgrounds, photometric variations, jitter, shadows, and large occlusions. We observe that these backgrounds often span multiple manifolds. Therefore, constraints that ensure continuity on those manifolds will result in better background estimation. Hence, we propose to incorporate the spatial and temporal sparse subspace clustering into the RPCA framework. To that end, we compute a spatial and temporal graph for a given sequence using motion-aware correlation coefficient. The information captured by both graphs is utilized by estimating the proximity matrices using both the normalized Euclidean and geodesic distances. The low-rank component must be able to efficiently partition the spatiotemporal graphs using these Laplacian matrices. Embedded with the RPCA objective function, these Laplacian matrices constrain the background model to be spatially and temporally consistent, both on linear and nonlinear manifolds. The solution of the proposed objective function is computed by using the LADMAP optimization scheme. Experiments are performed on challenging sequences from five publicly available datasets and are compared with 23 existing state-of-the-art methods. The results demonstrate excellent performance of the proposed algorithm for both background estimation and foreground segmentation.

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          Author and article information

          Journal
          IEEE Trans Image Process
          IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
          Institute of Electrical and Electronics Engineers (IEEE)
          1941-0042
          1057-7149
          Aug 29 2017
          Article
          10.1109/TIP.2017.2746268
          28866495
          0807135f-8200-4e7a-a1b5-7beea89d1117

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