Anomalous Human Behavior Detection Using a Network of RGB-D Sensors

  • Nicola Mosca
  • Vito Renò
  • Roberto Marani
  • Massimiliano Nitti
  • Fabio Martino
  • Tiziana D’Orazio
  • Ettore Stella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10188)


The detection of anomalous behaviors of people in indoor environments is an important topic in surveillance applications, especially when low cost solutions are necessary in contexts such as long corridors of public buildings, where standard cameras with long camera view would be either ineffective or costly to implement. This paper proposes a network of low cost RGB-D sensors with no overlapping fields-of-view, capable of identifying anomalous behaviors with respect a pre-learned normal one. A 3D trajectory analysis is carried out by comparing three different classifiers (SVM, neural networks and k-nearest neighbors). The results on real experiments prove the effectiveness of the proposed approach both in terms of performances and of real time application.


  1. 1.
    Almazan, E., Jones, G.: Tracking people across multiple non-overlapping RGB-D sensors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 831–837 (2013)Google Scholar
  2. 2.
    Bevilacqua, A., Di Stefano, L., Azzari, P.: People tracking using a time-of-flight depth sensor. In: 2006 IEEE International Conference on Video and Signal Based Surveillance, p. 89. IEEE (2006)Google Scholar
  3. 3.
    Bishop, C.M.: Pattern Recognition. Machine Learning, vol. 128. Springer, New York (2006)zbMATHGoogle Scholar
  4. 4.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)Google Scholar
  5. 5.
    Bouma, H., Baan, J., Landsmeer, S., Kruszynski, C., van Antwerpen, G., Dijk, J.: Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall. In: SPIE Defense, Security, and Sensing, p. 87560A. International Society for Optics and Photonics (2013)Google Scholar
  6. 6.
    Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)CrossRefGoogle Scholar
  7. 7.
    D’Orazio, T., Marani, R., Renò, V., Cicirelli, G.: Recent trends in gesture recognition: how depth data has improved classical approaches. Image Vis. Comput. 52, 56–72 (2016)CrossRefGoogle Scholar
  8. 8.
    D’Orazio, T., Guaragnella, C.: A survey of automatic event detection in multi-camera third generation surveillance systems. Int. J. Pattern Recogn. Artif. Intell. 29(01), 1555001 (2015)CrossRefGoogle Scholar
  9. 9.
    Fix, E., Hodges Jr., J.L.: Discriminatory analysis-nonparametric discrimination: consistency properties. Technical report, DTIC Document (1951)Google Scholar
  10. 10.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice- Hall, Englewood Cliffs (2004)zbMATHGoogle Scholar
  11. 11.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 34(3), 334–352 (2004)CrossRefGoogle Scholar
  12. 12.
    Kwon, B., Kim, D., Kim, J., Lee, I., Kim, J., Oh, H., Kim, H., Lee, S.: Implementation of human action recognition system using multiple kinect sensors. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 334–343. Springer, Cham (2015). Scholar
  13. 13.
    Nie, W., Liu, A., Su, Y.: Multiple person tracking by spatiotemporal tracklet association. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pp. 481–486. IEEE (2012)Google Scholar
  14. 14.
  15. 15.
    OpenNI: Openni website.
  16. 16.
    Piciarelli, C., Micheloni, C., Foresti, G.L.: Trajectory-based anomalous event detection. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1544–1554 (2008)CrossRefGoogle Scholar
  17. 17.
    Renò, V., Politi, T., D’Orazio, T., Cardellicchio, A.: An human perceptive model for person re-identification. In: VISAPP 2015. pp. 638–643. SCITEPRESS (2015)Google Scholar
  18. 18.
    Satta, R., Pala, F., Fumera, G., Roli, F.: Real-time appearance-based person re-identification over multiple kinectTM cameras. In: VISAPP (2), pp. 407–410 (2013)Google Scholar
  19. 19.
    Shu, G., Dehghan, A., Oreifej, O., Hand, E., Shah, M.: Part-based multiple-person tracking with partial occlusion handling. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1815–1821. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nicola Mosca
    • 1
  • Vito Renò
    • 1
  • Roberto Marani
    • 1
  • Massimiliano Nitti
    • 1
  • Fabio Martino
    • 1
  • Tiziana D’Orazio
    • 1
  • Ettore Stella
    • 1
  1. 1.National Research Council of Italy, Institute of Intelligent Systems for AutomationBariItaly

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