Abstract
Detecting moving objects from video frames is one of the key techniques in computer vision. Background subtraction is a common way to detect moving objects at present. A new background subtraction algorithm is proposed in this paper. The algorithm describes backgrounds by a combination of hue and improved local binary pattern (LBP) texture and adopts the idea of Gaussian mixture model that uses multiple modes to represent background. In order to reduce matching complexity and satisfy real-time, the LBP texture feature vectors are simplified. Experiments show that the proposed algorithm can satisfy real-time in common resolution videos, can remove effectively the effect of shadow and can detect moving objects more accurately than others.
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References
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252. IEEE, Fort Collins (1999)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ahonen, T., Matas, J., He, C., Pietikainen, M.: Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009)
Computer Vision and Robotics Research Laboratory Test bed Data, http://cvrr.ucsd.edu/aton/shadow/
Performance Evaluation of Tracking and Surveillance (PETS 2000), http://ftp.pets.rdg.ac.uk/pub/PETS2000/
Gonzalez, R.C., Richard, E.W.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
KaewTraKulPong, P.: Bowden. R.: An Improved Adaptive Background Mixture Model for Real-time Tracking and Shadow Detection. In: Proceedings of 2nd European Workshop on Advanced Video Based Surveillance, pp. 1–5. Kluwer Academic Publishers, Dordrecht (2001)
Heikkila, M., Pietikainen, M.: A Texture-based Method for Modeling the Background and Detecting Moving Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006)
Xu, J., Ding, X.Q., Wang, S.J., Wu, Y.S.: Background Subtraction Based on A Combination of Local Texture and Color. Acta Automatica Sinica 35(9), 1145–1150 (2009)
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Yuan, GW., Gao, Y., Xu, D., Jiang, MR. (2012). A New Background Subtraction Method Using Texture and Color Information. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_70
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DOI: https://doi.org/10.1007/978-3-642-25944-9_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
Online ISBN: 978-3-642-25944-9
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