Background Subtraction for Time of Flight Imaging
Keywords:industrial TOF cameras, machine vision, pattern recognition, support vector machines
A time of flight camera provides two types of images simultaneously, depth and intensity. In this paper a computational method for background subtraction, combining both images or fast sequences of images, is proposed. The background model is based on unbalanced or semi-supervised classifiers, in particular support vector machines. A brief review of one class support vector machines is first given. A method that combines the range and intensity data in two operational modes is then provided. Finally, experimental results are presented and discussed.
adaptive-K GMM: A survey,” International Journal of Advance Research in Computer Science
and Management Studies, vol. 2, pp. 300–308, 2014.
 M. Piccardi, “Background subtraction techniques: a review,” in IEEE International Conference on System. Man and Cybernetics, pp. 3099–3104, 2004.
 T. Bouwmans, L. Davis, J. Gonzalez, M. Piccardi, and C. Shan, “Special issue on background
modeling for foreground detection in real-world dynamic scenes,” Machine Vision and Applications, vol. 25, pp. 1101–1103, 2014.
 A. Vacavant, L. Tougne, and T. Chateau, “Special section on background models comparison,” Computer Vision and Image Understanding, vol. 122, pp. 1–202, 2014.
 C. Stauffer and W. L. Grimson, “Adaptive background mixture models for real-time tracking,” Computer Vision and Pattern Recognition, pp. 2246–2252, 1999.
 R. D. Cajote et al., “Framework of surveillance video analysis and transmission system using
background modeling and MIMO-OFDM,” in IEEE International Conference on Digital Signal Processing, pp. 1071–1075, 2015.
 F. Chiabrando, R. Chiabrando, D. Piatti, and F. Rinaudo, “Sensors for 3d imaging: Metric evaluation and calibration of a ccd/cmos time-offlight camera,” Sensors, vol. 9, pp. 10080–10096, 2009.
 A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors on computer graphics,” in Eurographics, 2009.
 S. Foix, G. Alenya, and C. Torras, “Lock-in time-of-flight (ToF) cameras: A survey,” Sensors, vol. 11, no. 9, pp. 1917–1926, 2011.
 T. Oggier et al., “Novel pixel architecture with inherent background suppression for 3d time-offlight imaging,” in Videometrics VIII, 2005.
 A. Glazer, M. Lindenbaum, and S. Markovitch, “One-class background model,” Lecture Notes in Computer Science, vol. 7728, pp. 301–307, 2012.
 S. H. Cho et al., “Background subtraction based object extraction for time-of-flight sensor,” in IEEE Global Conference on Consumer Electronics, pp. 48–49, 2013.
 J. Giacomantone, L. Violini, L. Lorenti, and A. D. Giusti, “Supresión de segundo plano en imágenes de tiempo de vuelo,” in XXII Congreso Argentino de Ciencias de la Computación, pp. 1064–1073, 2016.
 V. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988–999, 1999.
 D. J. Tax and R. P. W. Duin, “Support vector data description,” Machine Learning, vol. 54, pp. 45–
 B. Schlkopf, J. C. Platt, J. Shawe-Taylor, A. J. Samola, and R. C. Williamson, “Estimating the support of a high dimensional distribution,” Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001.
 F. Chiabrando, D. Piatti, and F. Rinaudo, “Sr4000 tof camera: Further experimental tests and
first applications to metric surveys,” in V Symposium on Remote Sensing and Spatial Information
Sciences, pp. 149–154, 2010.
 M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding, vol. 114, no. 12, pp. 1318–1328, 2010.
 M. Reynolds et al., “Capturing time-of-flight data with confidence,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 945–952, 2011.
 F. Lenzen et al., “Denoising strategies for timeof-flight data,” Lecture Notes in Computer Science, vol. 8200, pp. 25–45, 2013.
 A. Sabov and J. Kruguer, “Identification and correction of flying pixels in range camera data,” in
Computer Graphics, pp. 135–145, 2010.
 H. Rapp, M. Frank, F. Hamprecht, and B. Jahne, “A theoretical and experimental investigation of
the systematic errors and statistical uncertainties of time of flight cameras,” Intelligent Systems Technologies and Applications, vol. 5, pp. 402–413, 2008.
 H. Schöner, “Denoising 3d images from time-offlight cameras using extended anisotropic diffusion,” SPIE Newsroom, 2012.