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An algorithm to estimate mean vehicle speed from MPEG Skycam video

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Abstract

With low computation cost, motion vectors can be readily extracted from MPEG video streams and processed to estimate vehicle motion speed. A statistical model is proposed to model vehicle speed and noise. In order to achieve high estimation accuracy and also study the limitations of the proposed algorithm, we quantitatively evaluated four parameters used in our algorithm: temporal filter window size T, video resolution R v (CIF/QCIF), motion vector frame distance m, and video bit-rates. Our experiments showed that the mean vehicle speed can be estimated with high accuracy, up to 85 to 92% by proper spatial and temporal processing. The proposed algorithm is especially suitable for Skycam-based application, where the traditional tracking-based or virtual-loop-based approaches perform poorly because of their requirements of high-resolution images. Although extensive work has been done in extracting motion information directly from MPEG video data in compressed domain, to our best knowledge, this paper is the very first work in which stationary motion (speed) of moving objects can be estimated with high accuracy directly from MPEG motion vectors. Furthermore the proposed method is not limited to vehicle speed estimation by nature and it can be applied to other applications where the stationary motion assumption is satisfied.

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References

  1. Bartolini F, Cappellini V, Colombo C, Mecocci A (1993) Enhancement of local optic flow techniques. In: Proceedings 4th international workshop on time varying image proceeding and moving object recognition, pp.359–366, June 10–11, Florence, Italy

  2. Blake A, Curwen R, Zisserman A (1993) A framework for spatiotemporal control in the tracking of visual contours. Int J Comput Vis 11(2):127–146 (October)

    Article  Google Scholar 

  3. Dimitrova N, Glshani F (1995) Motion recovery for video content classification. ACM Trans Inf Sys 13(4):408–439 (October)

    Article  Google Scholar 

  4. Duda RO, Hart PE, Stock DG (2000) Pattern classification, 2nd edn. Wiley, New York, p 26

    Google Scholar 

  5. Eng H-L, Ma K-K (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10(2):242–251 (February)

    Article  MATH  Google Scholar 

  6. Garcia C, Tziritas G (2002) Optimal projection of 2-D displacements for 3-D translational motion estimation. Image Vis Comput 20:793–804

    Article  Google Scholar 

  7. Gonzales CA, Yeo H, Kuo CJ (1999) Requirements for motion estimation search range in MPEG-2 coded video. IBM J Res Develop 43(4):453-470 (July)

    Google Scholar 

  8. Harris C (1992) Tracking with rigid models. In: Blake A, Yuille A (eds) Active vision. MIT, Cambridge, MA

    Google Scholar 

  9. Information technology-coding of moving pictures and associated audio for digital storage media up to 1.5 Mbits/s: Video, ISO/IEC 11172-2, August 1993

  10. Jin JS, Zhu Z, Xu G (2000) A stable vision system for moving vehicles. IEEE Trans Intell Transp Syst 1(1):32–39 (March)

    Article  Google Scholar 

  11. Lai AHS, Yung NHC (2000) Vehicle-type identification through automated virtual loop assignment and block-based direction-biased motion estimation. IEEE Trans Intell Transp Syst 1(2):86–97 (June)

    Article  Google Scholar 

  12. Li H, Shi H (1999) A fast algorithm for reconstructing motion-compensated blocks in compressed domain. J Vis Lang Comput 10(6):607–623

    Article  MathSciNet  Google Scholar 

  13. Malik J, Russell S, Beymer D, Coifman B, Huang T, Liddy D, McLauchlan P (1997) Traffic surveillance and detection technology development: new traffic sensor technology: final report. University of California—PATH

  14. Marija J, Norusis/SPSS Inc (1992) SPSS for windows base system user’s guide release 5.0, p 183

  15. Meng J, Chang S-F (1996) CVEPS: a compressed video editing and parsing system. In: Proceedings of ACM Multimedia 96:43–53, Boston, MA (November)

  16. Michalopoulos PG (1991) Vehicle detection video through image processing: the autoscope system. IEEE Trans Veh Technol 40(1, Part 2):21–29 (February)

    Article  Google Scholar 

  17. Milanese R, Deguillaume F, Jacot-Descombes A (1997) Video segmentation and camera motion characterization using compressed data. In: Proc. SPIE Conf. on Multimedia Storage and Archiving Systems II, Dallas, TX, Nov. 2–7

  18. Patel NV, Sethi IK (1997) Video shot detection and characterization for video databases. Pattern Recogn 30(4):583–592 (April)

    Article  Google Scholar 

  19. Pilu M (1998) On using raw MPEG motion vectors to determine global camera motion with applications to image mosaicing. SPIE visual communications and image processing ‘98, San Jose, CA, USA, Jan

  20. Some Skycam websites: www.wthr.com/livecams/trafficcams.html; www.kgw.com/traffic/metrocams.html; www.eastciti.com/skycam.htm

  21. Stiller C, Konrad J (1999) Estimating motion in image sequences: a tutorial on modeling and computation of 2D motion. IEEE Signal Process Mag 16:70–91 (July)

    Article  Google Scholar 

  22. Wang H, Chang S-F (1997) A highly efficient system for automatic face region detection in MPEG video. IEEE Trans Circuits Syst Video Technol 7(4):615–628 (August)

    Article  MathSciNet  Google Scholar 

  23. Yeo B-L (1999) A fast microscopic browsing of MPEG-compressed video. Multimedia Syst 7:269–281

    Article  Google Scholar 

  24. Yoon K, DeMenthon D, Doermann D (2000) Event detection from MPEG video in the compressed domain. International conference on pattern recognition (ICPR ’00) 1:1819–1825, Barcelona, Spain, September 03–08

  25. Yu X, Duan L, Tian Q (2002) Highway traffic information extraction from Skycam MPEG video. In: Proceedings of IEEE 5th intelligent transportation systems conference (ITSC 2002), pp 37–42, Sep. 3–6

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Authors and Affiliations

  1. Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, Singapore, 639798

    Xiaodong Yu & Ping Xue

  2. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore, 119613

    Lingyu Duan & Qi Tian

Authors
  1. Xiaodong Yu
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  2. Ping Xue
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  3. Lingyu Duan
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  4. Qi Tian
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Correspondence to Xiaodong Yu.

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Yu, X., Xue, P., Duan, L. et al. An algorithm to estimate mean vehicle speed from MPEG Skycam video. Multimed Tools Appl 34, 85–105 (2007). https://doi.org/10.1007/s11042-006-0073-8

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  • DOI: https://doi.org/10.1007/s11042-006-0073-8

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