Multisubjects Tracking by Time-of-Flight Camera

  • Piercarlo Dondi
  • Luca Lombardi
  • Luigi Cinque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


Time-of-Flight cameras are the state of art sensors for a fast detection of depth data in a scene. This kind of sensors can be very useful for tracking, in particular in indoor ambient, since, using light in near-infrared spectrum, they are less affected by abrupt change in illumination. In this paper we propose a new method for the tracking of multiple subjects based on Kalman filter. The first step of our solution is a ToF based foreground segmentation, that retrieves all significant clusters in the scene, followed by a robust tracking system able to correctly handle occlusions and possible merging between clusters.


Tracking Time-of-Flight camera 


  1. 1.
    Oggier, T., Lehmann, M., Kaufmann, R., Schweizer, M., Richter, M., Metzler, P., Lang, G., Lustenberger, F., Blanc, N.: An all-solid-state optical range camera for 3D real-time imaging with sub-centimeter depth resolution (SwissRanger). In: Proceeding of the SPIE, vol. 5249, pp. 634–645 (2003)Google Scholar
  2. 2.
    Bianchi, L., Gatti, R., Lombardi, L., Lombardi, P.: Tracking without Background Model for Time-of-Flight Cameras. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 726–739. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Dondi, P., Lombardi, L.: Fast Real-Time Segmentation and Tracking of Multiple Subjects by Time-of-Flight Camera. In: 6th International Conference on Computer Vision Theory and Applications (VISAPP 2011), pp. 582–587 (2011)Google Scholar
  4. 4.
    Kolb, A., Barth, E., Koch, R., Larsen, R.: Time-of-Flight Cameras in Computer Graphics. Journal of Computer Graphics Forum 29, 141–159 (2010)CrossRefGoogle Scholar
  5. 5.
    CSEM: SwissRanger SR-3000 Manual, Mesa Imaging (2006)Google Scholar
  6. 6.
    Hansen, D.W., Hansen, M.S., Kirschmeyer, M., Larsen, R., Silvestre, D.: Cluster tracking with Time-of-Flight cameras. In: Proceedings of Computer Vision and Pattern Recognition Workshops (CVPRW 2008), pp. 1–6. IEEE Computer Society (2008)Google Scholar
  7. 7.
    Guomundsson, S.A., Larsen, R., Aanaes, H., Pardas, M., Casas, J.R.: TOF imaging in Smart room environments towards improved people tracking. In: Proceedings of Computer Vision and Pattern Recognition Workshops (CVPRW 2008), IEEE Computer Society (2008)Google Scholar
  8. 8.
    Bevilacqua, A., Di Stefano, L., Azzari, P.: People Tracking Using a Time-of-Flight Depth Sensor. In: Proceedings of the AVSS 2006, Video and Signal Based Surveillance, p. 89. IEEE Computer Society (2006)Google Scholar
  9. 9.
    Parvizi, E., Jonathan Wu, Q.M.: Multiple Object Tracking Based on Adaptive Depth Segmentation. In: Proceedings of Canadian Conference of Computer and Robot Vision, pp. 273–277. IEEE Computer Society (2008)Google Scholar
  10. 10.
    Sabeti, L., Parvizi, E., Jonathan Wu, Q.M.: Visual Tracking Using Color Cameras and Time-of-Flight Range Imaging Sensors. Journal of Multimedia 3(2), 28–36 (2008)CrossRefGoogle Scholar
  11. 11.
    Bleiweiss, A., Werman, M.: Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking. In: Kolb, A., Koch, R. (eds.) Dyn3D 2009. LNCS, vol. 5742, pp. 58–69. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Bianchi, L., Dondi, P., Gatti, R., Lombardi, L., Lombardi, P.: Evaluation of a foreground segmentation algorithm for 3D camera sensors. In: Foggia, P., Sansone, C., Vento, M. (eds.) ICIAP 2009. LNCS, vol. 5716, pp. 797–806. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Piercarlo Dondi
    • 1
  • Luca Lombardi
    • 1
  • Luigi Cinque
    • 2
  1. 1.Department of Electrical, Computer and Biomedical EngineeringUniversity of PaviaPaviaItaly
  2. 2.Department of Computer ScienceSapienza University of RomeRomaItaly

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