Abstract
Background subtraction is the first step in many video surveillance systems, its performance has a decisive influence on the result of the post-processing. An effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly, such as illumination changes or dynamic scenes. In this paper, a novel pixel-wise background subtraction algorithm is introduced. First, we propose a novel texture descriptor named Multi-Channel Scale Invariant Local Ternary Pattern(MC-SILTP). The pattern is cross-calculated in RGB color channels with the Scale Invariant Local Ternary Pattern operator. This descriptor does not only show an excellent performance in abundant texture regions, but also in flat regions. Secondly, we model each background pixel with a codebook rather than estimating the probability density functions. The codebook is consisted of many MC-SILTP samples actually observed in the past. A lot of experiments have been done over the proposed approach, results indicates that this approach is well balanced in sensitivity and robustness. It can handle the tricky problem of illumination changes robustly while detecting complete objects in flat areas sensitively. Comparison between the proposed one and several popular background subtraction algorithms demonstrates that it outperforms the state-of-the-art.
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References
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Elgammal, A., Duraiswami, R.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceeding of the IEEE 90, 1151–1163 (2002)
Sheikh, Y., Shah, M.: Bayesian modeling of dynamic scenes for object detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 27, 1778–1792 (2005)
Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the back-ground and detecting moving objects. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 657–662 (2006)
Hu, W., Li, X., Zhang, X., Shi, X., Maybank, S., Zhang, Z.: Incremental tensor subspace learning and its applications to foreground segmentation and tracking. International Journal of Computer Vision 91, 303–327 (2011)
Wang, L., Wu, H., Pan, C.: Adaptive ε LBP for Background Subtraction. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 560–571. Springer, Heidelberg (2011)
Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301–1306 (2010)
Liu, L., Sang, N., Huang, R.: Background subtraction using shape and colour information. Electronics Letters 46, 41–43 (2010)
Yao, J., Odobez, J.: Multi-layer background subtraction based on color and texture. In: IEEE Workshop on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Narayana, M., Hanson, A., Learned-Miller, E.: Background modeling using adaptive pixelwise kernel variances in a hubrid feature space. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)
Kim, K., Chalidabhongse, T.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing, vol. 5, pp. 3061–3064 (2004)
Prati, A., Mikic, I., Trivedi, M.: Detecting moving shadows:algorithms and evaluation. IEEE Transaction on Pattern Analysis and Machine Intelligence 25, 918–923 (2003)
Jabri, S., Duric, Z., Wechsler, H., Rosenfeld, A.: Detection and location of people in video images using adaptive fusion of color and edge information. In: Proceeding of the IEEE International Conference on Pattern Recognition, vol. 4, pp. 627–630 (2000)
Li, L., Huang, W., Gu, I., Tian, Q.: Foreground object detection from videos containing complex background. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 2–10 (2011)
Barnich, O., Van Droogenbroeck, M.: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing 20, 1709–1724 (2011)
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Ma, F., Sang, N. (2013). Background Subtraction Based on Multi-channel SILTP. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_7
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DOI: https://doi.org/10.1007/978-3-642-37410-4_7
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