Abstract
This paper presents a new approach for moving detection in complex scenes. Different with previous methods which compare a pixel with its own model and make the model more complex, we take an iterative model-sharing strategy as the process of foreground decision. The current pixel is not only compared with its own model, but may also compared with other pixel’s model which has similar temporal variation. Experiments show that the proposed approach leads to a lower false positive rate and higher precision. It has a better performance when compared with traditional approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hanzi, W., David, S.: A consensus-based method for tracking: modelling background scenario and foreground appearance. Pattern Recogn. 40, 1091–1105 (2007)
Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22, 809–830 (2000)
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19, 780–785 (1997)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1999, vol. 2. IEEE (1999)
Lee, D.S.: Effective gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27, 827–832 (2005)
Tuzel, O., Porikli, F., Meer, P.: A bayesian approach to background modeling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, CVPR Workshops 2005, p. 58. IEEE (2005)
Cristani, M., Farenzena, M., Bloisi, D., Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Sig. Process. 2010, 43 (2010)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11, 172–185 (2005)
Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Olivier, B., Marc, V.D.: Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20, 1709–1724 (2011)
Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38–43. IEEE (2012)
Shimada, A., Nagahara, H., Taniguchi, R.I.: Background modeling based on bidirectional analysis. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1979–1986. IEEE (2013)
Sobral, A.: Bgslibrary: an opencv c++ background subtraction library. In: IX Workshop de Viso Computacional (WVC 2013) (2013)
Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1937–1944. IEEE (2011)
Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 28–31. IEEE (2004)
Godbehere, A.B., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference (ACC), pp. 4305–4312. IEEE (2012)
Yao, J., Odobez, J.M.: Multi-layer background subtraction based on color and texture. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, Y., Zhao, K., Wu, W., Liu, S. (2015). Background Subtraction: Model-Sharing Strategy Based on Temporal Variation Analysis. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-16631-5_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16630-8
Online ISBN: 978-3-319-16631-5
eBook Packages: Computer ScienceComputer Science (R0)