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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.

[1] 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.

[2] 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.

[3] 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.

[4] Kolmogorov, V., Zabih, R., 2004. What energy functions can be minimized via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell., 26(2):147-159.

[5] Lu, L., Hager, G.D., 2007. A Nonparametric Treatment for Location/Segmentation Based Visual Tracking. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-8.

[6] 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.

[7] Parzen, E., 1962. On estimation of a probability density and mode. Ann. Math. Statist., 33(3):1065-1076.

[8] Sheikh, Y., Shah, M., 2005. Bayesian modeling of dynamic scenes for object detection. IEEE Trans. Pattern Anal. Mach. Intell., 27(11):1778-1792.

[9] Stauffer, C., Grimson, W., 2000. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):747-757.

[10] Sun, J., Zhang, W., Tang, X., Shum, H.Y., 2006. Background cut. LNCS, 3952:628-641.

[11] 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.

[12] Wang, Y., Tan, T., 2002. Adaptive Foreground and Shadow Detection in Image Sequences. Proc. Int. Conf. on Pattern Recognition, p.983-986.

[13] 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.