Abstract
Foreground detection plays a core role in a wide spectrum of applications such as tracking and behavior analysis. It, especially for videos captured by fixed cameras, can be posed as a component decomposition problem, the background of which is typically assumed to lie in a low dimensional subspace. However, in real world cases, dynamic backgrounds like waving trees and water ripples violate the assumption. Besides, noises caused by the image capturing process and, camouflage and lingering foreground objects would also significantly increase the difficulty of accurate foreground detection. That is to say, simply imposing the correlation constraint on the background is no longer sufficient for such cases. To overcome the difficulties mentioned above, this paper proposes to further take into account foreground characteristics including 1) the smoothness: the foreground object should appear coherently in spatial domain and move smoothly in temporal, and 2) the arbitrariness: the appearance of foreground could be with arbitrary colors or intensities. With the consideration of the smoothness and the arbitrariness of foreground as well as the correlation of (static) background, we formulate the problem in a unified framework from a probabilistic perspective, and design an effective algorithm to seek the optimal solution. Experimental results on both synthetic and real data demonstrate the clear advantages of our method compared to the state of the art alternatives.
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Babenko, B., Yang, M., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE TPAMI 33(8), 1619–1632 (2011)
Candès, E., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? Journal of the ACM 58(3), 1–37 (2011)
Cremers, D., Soatto, S.: Motion competition: A variational approach to piecewise parametric motion segmentation. IJCV 62(3), 249–265 (2005)
Cristani, M., Raghavendra, R., Bue, A.D., Murino, V.: Human behavior analysis in video surveillance: a social signal processing perspective. Neurocomputing 100, 86–97 (2013)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of IEEE 90(7), 1151–1163 (2002)
Haines, T., Xiang, T.: Background subtraction with dirichlet process mixture models. IEEE TPAMI (2014)
He, J., Balzano, L., Szlam, A.: Incremental gradient on the grassmannian for online foreground and background separation in subsampled video. In: CVPR, pp. 1568–1575 (2012)
Lee, D.: Effective gaussian mixture learning for video background subtraction. IEEE TPAMI 27(5), 827–832 (2005)
Li, L., Huang, W., Gu, I., Tian, Q.: Statistical modeling of complex backgrounds for foreground object detection. IEEE TIP 13(11), 1459–1472 (2004)
Lin, Z., Chen, M., Wu, L., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Tech. Rep. UILU-ENG-09-2215, UIUC Technical Report (2009)
Liu, J., Tai, X., Huang, H., Huan, Z.: A weighted dictionary learning model for denoising images corrupted by mixed noise. IEEE TIP 22(3), 1108–1120 (2013)
Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE TIP 17(7), 1168–1177 (2008)
Meng, D., De la Torre, F.: Robust matrix factorization with unknown noise. In: ICCV, pp. 1337–1344 (2013)
Peng, Y., Ganesh, A., Wright, J., Xu, W., Ma, Y.: Rasl: Robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE TPAMI 34(11), 2233–2246 (2012)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60(1), 259–268 (1992)
Srebro, N., Jaakkola, T.: Weighted low-rank approximations. In: ICML, pp. 720–727 (2003)
Stalder, S., Grabner, H., Van Gool, L.: Cascaded confidence filtering for improved tracking-by-detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. 246–252 (1999)
Vidal, R.: Subspace clustering. IEEE Signal Processing Magazine 28(2), 52–68 (2011)
Vidal, R., Ma, Y.: A unified algebraic approach to 2-D and 3-D motion segmentation. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 1–15. Springer, Heidelberg (2004)
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. IJCV 63(2), 153–161 (2005)
Wang, N., Yao, T., Wang, J., Yeung, D.-Y.: A probabilistic approach to robust matrix factorization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 126–139. Springer, Heidelberg (2012)
Wang, N., Yeung, D.: Bayesian robust matrix factorization for image and video processing. In: ICCV, pp. 1785–1792 (2013)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Realtime tracking of the human body. IEEE TPAMI 19(7), 780–785 (1997)
Xu, J., Ithapu, V., Mukherjee, L., Rehg, J., Singh, V.: GOSUS: Grassmanian online subspace updates with strutured-sparsity. In: ICCV, pp. 3376–3383 (2013)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4), 1–45 (2006)
Zhang, Z., Ganesh, A., Liang, X., Ma, Y.: Tilt: Transform invariant low-rank textures. IJCV 99(1), 1–24 (2012)
Zhou, T., Tao, D.: Godec: Randomized low-rank & sparse matrix decomposition in noisy case. In: ICML, pp. 33–40 (2011)
Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE TPAMI 35(3), 597–610 (2013)
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Guo, X., Wang, X., Yang, L., Cao, X., Ma, Y. (2014). Robust Foreground Detection Using Smoothness and Arbitrariness Constraints. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham. https://doi.org/10.1007/978-3-319-10584-0_35
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DOI: https://doi.org/10.1007/978-3-319-10584-0_35
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