Model of Surveillance System Based on Sound Tracking

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)


Research in the audio and video surveillance is gaining more popularity due to its wide-spread applications as well as social impact. There have been considerable efforts which are focused on developing various algorithms and models for surveillance systems. Recently, the attention in audio and video surveillance has turned towards to design autonomous detection system. In this paper a surveillance system based on sound source localization by a microphone array is presented. The design of microphone array has an important issue in the accuracy of source localization. For the surveillance application, beamforming algorithms are implemented to increase the tracking and detection performance. The real-time capabilities of an automated surveillance system are analyzed, providing a performance analysis of the localization system under different acoustic conditions.


Beamforming Microphone array Signal processing Surveillance 



This work was supported in frame of Internal Grant Agency of Tomas Bata University in Zlin, Faculty of Applied Informatics IGA/CebiaTech/2015/005 and IGA/FAI/2016/005.


  1. 1.
    Ngo, H.T., Ives, R.W., Rakvic, R.N., Broussard, R.P.: Real-time video surveillance on an embedded, programmable platform. Microprocess. Microsyst. 37(6–7), 562–571 (2013). doi: 10.1016/j.micpro.2013.06.003CrossRefGoogle Scholar
  2. 2.
    Habib, T.: Doctoral thesis auditory inspired methods for multiple speaker localization and tracking using a circular microphone array (2011)Google Scholar
  3. 3.
    Denman, S., Kleinschmidt, T., Ryan, D., Barnes, P., Sridharan, S., Fookes, C.: Automatic surveillance in transportation hubs: no longer just about catching the bad guy. Expert Syst. Appl. 42(24), 9449–9467 (2015). doi: 10.1016/j.eswa.2015.08.001CrossRefGoogle Scholar
  4. 4.
    Kim, H., Lee, S., Kim, Y., Lee, S., Lee, D., Ju, J., Myung, H.: Weighted joint-based human behavior recognition algorithm using only depth information for low-cost intelligent video-surveillance system. Expert Syst. Appl. 45, 131–141 (2016). doi: 10.1016/j.eswa.2015.09.035CrossRefGoogle Scholar
  5. 5.
    Zwyssig, E.: Speech processing using digital MEMS microphones. Doctor of Philosophy Centre for Speech Technology Research School of Informatics, University of Edinburgh (2013)Google Scholar
  6. 6.
    Papez, M., Vlcek K.: Enhanced MVDR beamforming for MEMS microphone array, MCSI, 2015. In: 2015 International Conference on Mathematics and Computers in Sciences and in Industry (MCSI). ISBN: 978-147994324-1Google Scholar
  7. 7.
    Bitzer, J., Simmer, K.U.: Superdirective microphone arrays. In: Microphone Arrays, pp. 19–38. Springer (2001)Google Scholar
  8. 8.
    Elko, G.W., Meyer, J.: Microphone arrays. In: Springer Handbook of Speech Processing, pp. 1021–1041. Springer (2008)Google Scholar
  9. 9.
    Balasem, S., Tiong, S., Koh, S.: Beamforming algorithms technique by using MVDR and LCMV. World Appl. Program. 2(5), 315–324 (2011).
  10. 10.
    Doblinger, G.: An adaptive microphone array for optimum beamforming and noise reduction. In European Signal Processing Conference (2006)Google Scholar
  11. 11.
    Doclo, S., Moonen, M.: Superdirective beamforming robust against microphone mismatch. IEEE Trans. Audio Speech Lang. Process. 15(2), 617–631 (2007). doi: 10.1109/TASL.2006.881676CrossRefGoogle Scholar
  12. 12.
    Krim, H., Viberg, M.: Two decades of array signal processing research: the parametric approach. IEEE Signal Process. Mag. 13, 67–94 (1996)CrossRefGoogle Scholar
  13. 13.
    Brandstein, M.S., Ward, D.B. (eds.): Microphone Arrays: Signal Processing Techniques and Applications. Springer-Verlag, Heidelberg (2001)Google Scholar
  14. 14.
    Benesty, J., Chen, J., Huang, Y.: Springer Topics in signal processing: microphone array signal processing. In: Benesty, J., Kellermann, W. (eds.). Springer, Heidelberg (2008)Google Scholar
  15. 15.
    Compton, R.: Adaptive Antennas Concept and Performance. Prentice Hall (2011)Google Scholar
  16. 16.
    Bellofiore, S., Foutz, J., Balanis, C.A., Spanias, A.S.: Smart-antenna system for mobile communication networks Part 2: beamforming and network throughput IEEE Antennas Propag. Mag. 44(4) (2002)Google Scholar
  17. 17.
    Chen, Z., Li, Z., Wang, S., Yin, F.: A microphone position calibration method based on combination of acoustic energy decay model and TDOA for distributed microphone array. Appl. Acoust. 95, 13–19 (2015). doi: 10.1016/j.apacoust.2015.02.013CrossRefGoogle Scholar
  18. 18.
    Friedland, G., Janin, A., Imseng, D., Anguera, X., Gottlieb, L., Huijbregts, M., Knox, M., Vinyals, O.: The ICSI RT-09 speaker diarization system. IEEE Trans. Audio Speech Lang. Process. 20(2), 371–381 (2012)CrossRefGoogle Scholar
  19. 19.
    Zhu, G., Xie, H., Zhou, Y., Xie, H., Zhou, Y.: Author ’ s accepted manuscript to appear in : signal processing (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Faculty of Applied Informatics, Department of Computer and Communication SystemsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations