High Frame Rate Real-Time Scene Change Detection System

  • Sanjay SinghEmail author
  • Ravi Saini
  • Sumeet Saurav
  • Pramod Tanwar
  • Kota S. Raju
  • Anil K. Saini
  • Santanu Chaudhury
  • Idaku Ishii
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)


Scene change detection, one of the fundamental and most important problem of computer vision, plays a very important role in the realization of a complete industrial vision system as well as automated video surveillance system - for automatic scene analysis, monitoring, and generation of alerts based on relevant changes in a video stream. Therefore, in addition to being accurate and robust, a successful scene change detection system must also be of very high frame rate in order to detect scene changes which goes off within a glimpse of the eye and often goes unnoticeable by the conventional frame rate cameras. Keeping the high frame rate processing as main focus, a very high frame rate real-time scene change detection system is developed by leveraging VLSI design to achieve high performance. This is accomplished by proposing, designing, and implementing an area-efficient scene change detection VLSI architecture on FPGA-based IDP Express platform. The developed prototype of complete real-time scene change detection system is capable of processing 2000 frames per second for 512 × 512 video resolution and is tested for live incoming video streams from high speed camera. The proposed and implemented system architecture is adaptable and scalable for different video resolutions and frame rates.


High speed scene change detection VLSI architecture FPGA implementation Automated video surveillance system 



Sanjay Singh is thankful to Prof. Raj Singh, Chief Scientist and Group Leader, IC Design Group, CSIR-CEERI, Pilani and Dr. A.S. Mandal, Chief Scientist, CSIR-CEERI, Pilani for their constant support and motivation. The financial support of Ministry of Electronics & Information Technology (MeitY), Govt. of India is gratefully acknowledged.


  1. 1.
    Chutani, E.R., Chaudhury, S.: Video trans-coding in smart camera for ubiquitous multimedia environment. In: Proceedings: International Symposium on Ubiquitous Multimedia Computing, pp. 185–189 (2008)Google Scholar
  2. 2.
    Rosin, P.L.: Thresholding for change detection. In: Proceedings: Sixth International Conference on Computer Vision, pp. 274–279 (1998)Google Scholar
  3. 3.
    Rosin, P.L., Ioannidis, E.: Evaluation of global image thresholding for change detection. Pattern Recogn. Lett. 24(14), 2345–2356 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Smits, P.C., Annoni, A.: Toward specification-driven change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1484–1488 (2000)CrossRefGoogle Scholar
  5. 5.
    Radke, R.J., Andra, S., Kofahi, O.A., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294–307 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Cavallaro, A., Ebrahimi, T.: Video object extraction based on adaptive background and statistical change detection. In: Proceedings: SPIE Visual Communications and Image Processing, pp. 465–475 (2001)Google Scholar
  7. 7.
    Huwer, S., Niemann, H.: Adaptive change detection for real-time surveillance applications. In: Proceedings: Third IEEE International Workshop on Visual Surveillance, pp. 37–46 (2000)Google Scholar
  8. 8.
    Kanade, T., Collins, R.T., Lipton, A.J., Burt, P., Wixson, L.: Advances in cooperative multi-sensor video surveillance. In: Proceedings: DARPA Image Understanding Workshop, pp. 3–24 (1998)Google Scholar
  9. 9.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  10. 10.
    Butler, D.E., Bove, V.M., Sridharan, S.: Real-time adaptive foreground/background segmentation. EURASIP J. Appl. Signal Process. 2005, 2292–2304 (2005)CrossRefzbMATHGoogle Scholar
  11. 11.
  12. 12.
    Kristensen, F., Hedberg, H., Jiang, H., Nilsson, P., Öwall, V.: An embedded real-time surveillance system: implementation and evaluation. J. Signal Process. Syst. 52(1), 75–94 (2008)CrossRefGoogle Scholar
  13. 13.
    Jiang, H., Ardö, H., Öwall, V.: A hardware architecture for real-time video segmentation utilizing memory reduction techniques. IEEE Trans. Circuits Syst. Video Technol. 19(2), 226–236 (2009)CrossRefGoogle Scholar
  14. 14.
    Genovese, M., Napoli, E., Petra, N.: OpenCV compatible real time processor for background foreground identification. In: Proceedings: International Conference on Microelectronics, pp 467–470 (2010)Google Scholar
  15. 15.
    Genovese, M., Napoli, E.: FPGA-based architecture for real time segmentation and denoising of HD video. J. Real Time Image Process. 8(4), 389–401 (2013)CrossRefGoogle Scholar
  16. 16.
    Genovese, M., Napoli, E.: ASIC and FPGA implementation of the Gaussian mixture model algorithm for real-time segmentation of high definition video. IEEE Trans. Very Large Scale Integr. 22(3), 537–547 (2014)CrossRefGoogle Scholar
  17. 17.
    Singh, S., Shekhar, C., Vohra, A.: FPGA-based real-time motion detection for automated video surveillance systems. Electronics 5(1), 1–18 (2016). MDPI. Article No. 10CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sanjay Singh
    • 1
    Email author
  • Ravi Saini
    • 1
  • Sumeet Saurav
    • 1
  • Pramod Tanwar
    • 1
  • Kota S. Raju
    • 1
  • Anil K. Saini
    • 1
  • Santanu Chaudhury
    • 1
  • Idaku Ishii
    • 2
  1. 1.CSIR-Central Electronics Engineering Research Institute (CSIR-CEERI), Academy of Scientific and Innovative Research (AcSIR)PilaniIndia
  2. 2.Robotics LaboratoryHiroshima UniversityHiroshimaJapan

Personalised recommendations