Abstract
Foreground detection is a fundamental task in video processing. Recently, many background subspace estimation based foreground detection methods have been proposed. In this paper, a sparse error compensation based incremental principal component analysis method, which robustly updates background subspace and estimates foreground, is proposed for foreground detection. There are mainly two notable features in our method. First, a sparse error compensation process via a probability sampling procedure is designed for subspace updating, which reduces the interference of undesirable foreground signal. Second, the proposed foreground detection method could operate without an initial background subspace estimation, which enlarges the application scope of our method. Extensive experiments on multiple real video sequences show the superiority of our method.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant No.61273273, 61175096 and 61271374, the Specialized Fund for Joint Bulding Program of Beijing Municipal Education Commission, and the Research Fund for Doctoral Program of Higher Education of China under Grant No. 20121101110043.
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Qin, M., Lu, Y., Di, H., Zhou, T. (2015). A Sparse Error Compensation Based Incremental Principal Component Analysis Method for Foreground Detection. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_23
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DOI: https://doi.org/10.1007/978-3-319-24075-6_23
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