Advertisement

The Architecture of an Embedded Smart Camera for Intelligent Inspection and Surveillance

  • Michał Fularz
  • Marek Kraft
  • Adam Schmidt
  • Andrzej Kasiński
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 350)

Abstract

Real time video surveillance and inspection is complex task, requiring processing large amount of image data. Performing this task in each node of a multi-camera system requires high performance and power efficient architecture of the smart camera. Such solution, based on a Xilinx Zynq heterogeneous FPGA (Field Programmable Logic Array) is presented in this paper. The proposed architecture is a general foundation, which allows easy and flexible prototyping and implementation of a range of image and video processing algorithms. Two example algorithm implementations using the described architecture are presented for illustration – moving object detection and feature points detection, description and matching.

Keywords

Smart Camera Embedded System Computer Vision Hardware Software Codesign FPGA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbo, A., Kleihorst, R., Choudhary, V., Sevat, L., Wielage, P., Mouy, S., Vermeulen, B., Heijligers, M.: Xetal-II: A 107 GOPS, 600 mW massively parallel processor for video scene analysis. IEEE Journal of Solid-State Circuits 43(1), 192–201 (2008)CrossRefGoogle Scholar
  2. 2.
    Aghajan, H., Cavallaro, A.: Multi-camera networks: principles and applications. Academic Press (2009)Google Scholar
  3. 3.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: A survey. Computer Networks 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bailey, D.G.: Design for embedded image processing on FPGAs. John Wiley & Sons (2011)Google Scholar
  5. 5.
    Bhanu, B., Ravishankar, C., Roy-Chowdhury, A., Aghajan, H., Terzopoulos, D.: Distributed Video Sensor Networks. Springer (2011)Google Scholar
  6. 6.
    Birem, M., Berry, F.: DreamCam: A modular FPGA-based smart camera architecture. Journal of Systems Architecture 60(6), 519–527 (2014)CrossRefGoogle Scholar
  7. 7.
    Bourrasset, C., Serot, J., Berry, F.: FPGA-based smart camera mote for pervasive wireless network. In: Proc. of International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (October 2013)Google Scholar
  8. 8.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: Computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1281–1298 (2012)CrossRefGoogle Scholar
  9. 9.
    Chen, P., Ahammad, P., Boyer, C., Huang, S.I., Lin, L., Lobaton, E., Meingast, M., Oh, S., Wang, S., Yan, P., et al.: CITRIC: A low-bandwidth wireless camera network platform. In: Proc. of International Conference on Distributed Smart Cameras, pp. 1–10. IEEE (2008)Google Scholar
  10. 10.
    Chen, P., Hong, K., Naikal, N., Sastry, S.S., Tygar, D., Yan, P., Yang, A.Y., Chang, L.C., Lin, L., Wang, S., Lobatón, E., Oh, S., Ahammad, P.: A low-bandwidth camera sensor platform with applications in smart camera networks. ACM Transactions on Sensor Networks 9(2), 21:1–21:23 (2013)CrossRefGoogle Scholar
  11. 11.
    Di Caterina, G., Hunter, I., Soraghan, J.: An embedded smart surveillance system for target tracking using a PTZ camera. In: 2010 4th European Education and Research Conference (EDERC), pp. 165–169 (December 2010)Google Scholar
  12. 12.
    Fularz, M., Kraft, M., Kasinski, A., Acasandrei, L.: A hybrid system on chip solution for the detection and labeling of moving objects in video streams. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 94–99 (September 2013)Google Scholar
  13. 13.
    Fularz, M., Kraft, M., Schmidt, A., Kasiński, A.: FPGA implementation of the robust essential matrix estimation with RANSAC and the 8-point and the 5-point method. In: Keller, R., Kramer, D., Weiss, J.-P. (eds.) Facing the Multicore - Challenge II. LNCS, vol. 7174, pp. 60–71. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Hengstler, S., Prashanth, D., Fong, S., Aghajan, H.: Mesheye: A hybrid-resolution smart camera mote for applications in distributed intelligent surveillance. In: 6th International Symposium on Information Processing in Sensor Networks, IPSN 2007, pp. 360–369 (April 2007)Google Scholar
  15. 15.
    Jin, X., Goto, S.: Encoder adaptable difference detection for low power video compression in surveillance system. Signal Processing: Image Communication 26(3), 130–142 (2011)Google Scholar
  16. 16.
    Kandhalu, A., Rowe, A., Rajkumar, R.: Dspcam: A camera sensor system for surveillance networks. In: Third ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009, pp. 1–7 (August 2009)Google Scholar
  17. 17.
    Kraft, M., Fularz, M., Kasiński, A.: System on chip coprocessors for high speed image feature detection and matching. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 599–610. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Ma, T., Hempel, M., Peng, D., Sharif, H.: A survey of energy-efficient compression and communication techniques for multimedia in resource constrained systems. IEEE Communications Surveys Tutorials 15(3), 963–972 (2013)CrossRefGoogle Scholar
  19. 19.
    Maggiani, L., Salvadori, C., Petracca, M., Pagano, P., Saletti, R.: Reconfigurable FPGA architecture for computer vision applications in smart camera networks. In: 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), pp. 1–6 (October 2013)Google Scholar
  20. 20.
    McFarlane, N., Schofield, C.: Segmentation and tracking of piglets in images. Machine Vision and Applications 8(3), 187–193 (1995)CrossRefGoogle Scholar
  21. 21.
    Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Seema, A., Reisslein, M.: Towards efficient wireless video sensor networks: A survey of existing node architectures and proposal for a Flexi-WVSNP design. IEEE Communications Surveys Tutorials 13(3), 462–486 (2011)CrossRefGoogle Scholar
  23. 23.
    Tomasi, M., Pundlik, S., Luo, G.: FPGA–DSP co-processing for feature tracking in smart video sensors. Journal of Real-Time Image Processing, 1–17 (2014)Google Scholar
  24. 24.
    Winkler, T., Rinner, B.: Pervasive smart camera networks exploiting heterogeneous wireless channels. In: IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, pp. 1–4 (March 2009)Google Scholar
  25. 25.
    Winkler, T., Rinner, B.: Trustcam: Security and privacy-protection for an embedded smart camera based on trusted computing. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 593–600 (August 2010)Google Scholar
  26. 26.
    Xilinx Inc.: AXI Reference Guide, Version 13.4 (2012)Google Scholar
  27. 27.
    Xilinx Inc.: Zynq-7000 all programmable SoC overview (August 2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michał Fularz
    • 1
  • Marek Kraft
    • 1
  • Adam Schmidt
    • 1
  • Andrzej Kasiński
    • 1
  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

Personalised recommendations