Selective Motion Estimation for Surveillance Videos

  • Muhammad Akram
  • Naeem Ramzan
  • Ebroul Izquierdo
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 40)


In this paper, we propose a novel approach to perform efficient motion estimation specific to surveillance videos. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Two approaches for selective motion estimation, GOP-by-GOP and Frame-by-Frame, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when moving object is detected for any frame of the GOP. While for the latter approach; each frame is tested for the motion activity and consequently for selective motion estimation. Experimental evaluation shows that significant reduction in computational complexity can be achieved by applying the proposed strategy.


Fast motion estimation surveillance video background subtraction block matching algorithm 


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  1. 1.
    Li, R., Zeng, B., Liou, M.L.: A New Three-Step Search Algorithm for Block Motion Estimation. IEEE Trans. Circuit Syst. Video Technol. 4, 438–442 (1994)CrossRefGoogle Scholar
  2. 2.
    Po, L.M., Ma, W.C.: A Novel Four Step Search Algorithm for Fast Block Motion Estimation. IEEE Trans. Circuit Syst. Video Technol. 6, 313–317 (1996)CrossRefGoogle Scholar
  3. 3.
    Zhu, S., Ma, K.K.: A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation. IEEE Trans. Image Processing. 9, 287–290 (2000)CrossRefGoogle Scholar
  4. 4.
    Lam, C.W., Po, L.M., Cheung, C.H.: A Novel Kite-Cross-Diamond Search Algorithm for Fast Block Matching Motion Estimation. In: IEEE ISCAS, vol. 3, pp. 729–732 (2004)Google Scholar
  5. 5.
    Yi, X., Ling, N.: Rapid Block-Matching Motion Estimation Using Modified Diamond Search. In: IEEE ISCAS, vol. 6, May 2005, pp. 5489–5492 (2005)Google Scholar
  6. 6.
    Stauffer, C., Grimson, W.E.L.: Learning Patterns of Activity Using Real Time Tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 22, 747–757 (2000)CrossRefGoogle Scholar
  7. 7.
    Mrak, M., Sprljan, N., Zgaljic, T., Ramzan, N., Wan, S., Izquierdo, E.: Performance Evidence of Software Proposal for Wavelet Video Coding Exploration Group. Technical Report, ISO/IEC JTC1/SC29/WG11/MPEG2006/ 13146 (2006)Google Scholar
  8. 8.
    Zgaljic, T., Ramzan, N., Akram, M., Izquierdo, E., Caballero, R., Finn, A., Wang, H., Xiong, Z.: Surveillance Centric Coding. In: 5th International Conference on Visual Information Engineering (VIE 2008), July 2008, pp. 835–839 (2008)Google Scholar
  9. 9.
    Akram, M., Ramzan, N., Izquierdo, E.: Event Based Video Coding Architecture. In: 5th International Conference on Visual Information Engineering (VIE 2008), July 2008, pp. 807–812 (2008)Google Scholar
  10. 10.
    Mrak, M., Izquierdo, E.: Spatially Adaptive Wavelet Transform for Video Coding with Multi-Scale Motion Compensation. In: IEEE International Conference on Image Processing, September 2007, vol. 2, pp. 317–3320 (2007)Google Scholar
  11. 11.
    Zgaljic, T., Sprljan, N., Izquierdo, E.: Bit-Stream Allocation Methods for Scalable Video Coding Supporting Wireless Communications. Signal Processing: Image Communications 22, 298–316 (2007)Google Scholar
  12. 12.
    Recommendation ITU-T BT 500.10: Methodology for the Subjective Assessment of the Quality of Televisions Pictures (2000) Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Muhammad Akram
    • 1
  • Naeem Ramzan
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Electronic Engineering DepartmentQueen Mary University of LondonUnited Kingdom

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