Abstract
Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.
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References
Deng, Y., Kenney, C., Moore, M.S., Manjunath, B.S., 1999. Peer Group Filtering and Perceptual Color Image Quantization. Proc. IEEE Int. Symp. on Circuits and Systems, 4:21–24. [doi:10.1109/ISCAS.1999.779933]
Elgammal, A., Harwood, D., Davis, L., 2002. Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE, 90(7): 1151–1163. [doi:10.1109/JPROC.2002.801448]
Greig, D., Porteous, B., Seheult, A., 1989. Extract maximum a posteriori estimation for binary images. J. Royal Stat. Soc. Ser. B, 51(2):271–279.
Kolmogorov, V., Zabih, R., 2004. What energy functions can be minimized via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 26(2):147–159. [doi:10.1109/TPAMI.2004.1262177]
Lu, L., Hager, G.D., 2007. A Nonparametric Treatment forLocation/Segmentation Based Visual Tracking. IEEE Conf. on Computer Vision and Pattern Recognition, p.1–8. [doi:10.1109/CVPR.2007.382976]
Mahamud, S., 2006. Comparing Belief Propagation and Graph Cuts for Novelty Detection. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.1154–1159. [doi:10.1109/CVPR.2006.80]
Parzen, E., 1962. On estimation of a probability density and mode. Ann. Math. Statist., 33(3):1065–1076. [doi:10.1214/aoms/1177704472]
Sheikh, Y., Shah, M., 2005. Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Mach. Intell., 27(11):1778–1792. [doi:10.1109/TPAMI. 2005.213]
Stauffer, C., Grimson, W., 2000. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach Intell., 22(8):747–757. [doi:10.1109/34.868677]
Sun, J., Zhang, W., Tang, X., Shum, H.Y., 2006. Background cut. LNCS, 3952:628–641. [doi:10.1007/11744047_48]
Wand, M., Jones, M., 1995. Kernel Smoothing. In: Cox, D.R., Hinkley, D.V., Reid, N., et al. (Eds.), Monographs on Statistics and Applied Probability. Chapman and Hall, New York.
Wang, Y., Tan, T., 2002. Adaptive Foreground and Shadow Detection in Image Sequences. Proc. Int. Conf. on Pattern Recognition, p.983–986.
Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.P., 1997. Pfinder: real time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):780–785. [doi:10.1109/34.598236]
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Project (Nos. 60602012 and 60675023) supported by the National Natural Science Foundation of China, the National High-Tech Research and Development Program (863) of China (No. 2007AA01Z 164), and the Shanghai Key Laboratory Opening Plan Grant (No. 06dz22103), China
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Yu, Sy., Wang, Fl., Xue, Yf. et al. Bayesian moving object detection in dynamic scenes using an adaptive foreground model. J. Zhejiang Univ. Sci. A 10, 1750–1758 (2009). https://doi.org/10.1631/jzus.A0820743
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DOI: https://doi.org/10.1631/jzus.A0820743