Abstract
In this paper we propose to join the benefits of multiple invariant information into the well-know background subtraction method ”Codebook”. Indeed, this method mainly repose on a color model allowing a separate process of color and intensity distortion. In order to manage hard situations involving high illumination changes, we propose to enhance this model with the use of two supplementary steps: 1/ transforming the input color image using a colorimetric invariant in order to obtain a color-invariant image whatever the illumination conditions; 2/ using depth information as a new data inside the Codebook model, thus performing an RGB-D fusion during the segmentation process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Automatic color constancy algorithm selection and combination. Pattern Recognition 43(3), 695–705 (2010)
Bouwmans, T., Baf, F.E.: Statistical background modeling for foreground detection: A survey. In: Handbook of Pattern Recognition and Computer (2010)
Bouwmans, T., Baf, F.E., Vachon, B., et al.: Background modeling using mixture of gaussians for foreground detection-a survey (2008)
Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute (1980)
Buchsbaum, W.H.: Color TV Servicing, third edition. Prentice Hall, Englewood Cliffs (1975)
Carron, T.: Segmentation d’images couleur dans la base Teinte Luminance Saturation: approche numerique et symbolique. PhD thesis, Universite de Stanford (1995)
Truong Cong, D.-N., Khoudour, L., Achard, C., Meurie, C., Lezoray, O.: People re-identification by spectral classification of silhouettes. Signal Processing 90(8), 2362–2374 (2010), Special Section on Processing and Analysis of High-Dimensional Masses of Image and Signal Data
Fernandez-Sanchez, E.J., Diaz, J., Ros, E.: Background subtraction based on color and depth using active sensors. Sensors 13(7), 8895–8915 (2013)
Finlayson, G.D., Hordley, S.D., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. In: Pattern Recognition (2005)
Finlayson, G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization (1998)
Gevers, T.: Arnold W.M. Smeulders. Color-based object recognition. Pattern Recognition (1999)
Gijsenij, A., Gevers, T.: Color constancy using natural image statistics and scene semantics. IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Kim, K., Chalidabhongse, T.H., Hanuood, D., Davis, L.: Background modeling and substraction by codebook construction (2004)
Leykin, A.: Robust multi-pedestrian tracking in thermal-visible surveillance videos. In: In and Beyond the Visible Spectrum Workshop at the International Conference on Computer Vision and Pattern Recognition, vol. 136, pp. 0–136 (2006)
Mcivor, A.M.: Background Subtraction Techniques (2000)
Murgia, J., Meurie, C., Ruichek, Y.: Improvement of moving objects detection in continued all-day illumination conditions using color invariants and color spaces (2013)
Obdrzalek, S., Matas, J., Chum, O.: On the interaction between object recognition and colour constancy. In: Proc. International Workshop on Color and Photometric Methods in Computer Vision (2003)
Salmane, H., Ruichek, Y., Khoudour, L.: Gaussian Propagation Model Based Dense Optical Flow for Objects Tracking. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part I. LNCS, vol. 7324, pp. 234–244. Springer, Heidelberg (2012)
Salmane, H., Ruichek, Y., Khoudour, L.: Using Hidden Markov Model and Dempster-Shafer Theory for Evaluating and Detecting Dangerous Situations in Level Crossing Environments. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012, Part I. LNCS, vol. 7629, pp. 131–145. Springer, Heidelberg (2013)
Smith, A.R.: Color gamut transform pairs. In: SIGGRAPH Comput. Graph (1978)
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, vol. 2, p. 2246 (1999)
Zivkovic, Z., van der Heijden, F.: Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 651–656 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Murgia, J., Meurie, C., Ruichek, Y. (2014). An Improved Colorimetric Invariants and RGB-Depth-Based Codebook Model for Background Subtraction Using Kinect. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-13647-9_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13646-2
Online ISBN: 978-3-319-13647-9
eBook Packages: Computer ScienceComputer Science (R0)