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User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis

  • Albert Clapés
  • Miguel Reyes
  • Sergio Escalera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7378)

Abstract

We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.

Keywords

Multi-modal RGB-Depth data analysis User identification Object Recognition Visual features Statistical learning 

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References

  1. 1.
    Lipton, A.J., Fujiyoshi, H.: Moving target classification and tracking from real-time video. In: Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, WACV 1998, pp. 8–14 (1998)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Brown, L.M., Senior, A.W., Li Tian, Y., Connell, J., Hampapur, A., Fe Shu, C., Merkl, H., Lu, M.: Performance evaluation of surveillance systems under varying conditions. In: Proceedings of IEEE PETS Workshop, pp. 1–8 (2005)Google Scholar
  4. 4.
    Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images (2011)Google Scholar
  5. 5.
    Rusu, R.B.: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. Articial Intelligence (KI - Kuenstliche Intelligenz) (2010)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Local feature view clustering for 3d object recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 682–688 (2001)Google Scholar
  8. 8.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Albert Clapés
    • 1
    • 2
  • Miguel Reyes
    • 1
    • 2
  • Sergio Escalera
    • 1
    • 2
  1. 1.Dept. Matemàtica Aplicada i AnàlisiUniversitat de BarcelonaBarcelonaSpain
  2. 2.Computer Vision CenterBellaterraSpain

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