Skip to main content
SpringerLink
Log in

FSE-MV: Compressed Domain Video Information Assisted Hybrid Real-Time Vehicle Speed Estimation

  • Conference paper
  • First Online:
Mobile Networks and Management (MONAMI 2021)

Included in the following conference series:

  • 576 Accesses

Abstract

Vehicular speed estimation is a vital component in intelligent transportation systems. With the recent development of smart cameras and computer vision technologies, video-based vehicle speed estimations have been widely studied. However, facing the huge volume of pixel-domain information, conventional methods are computationally intensive, and often fail to deliver estimation results in real-time. In this paper, we target the video-based real-time vehicle speed estimation problem. For data volume reduction, we utilize the compressed domain video information and propose a hybrid real-time vehicle speed estimation method termed FSE-MV. FSE-MV first segments vehicles using motion vector (MV) information in the compressed domain. The pixel information of the segmented vehicles is then retrieved through decoding. Feature points of each vehicle are extracted for multi-object matching and pixel domain displacement calculation. The speed of the target vehicle is finally calculated through spatial coordinate transformation. Experiments over the public dataset demonstrate that FSE-MV is able to process 1080p traffic video data in real-time (\(\thicksim \)30 frames per second) with a high estimation accuracy (\(\thicksim \)93.09%).

This work is supported by Collaborative Innovation Major Project of Zhengzhou under Grant 20XTZX06013, the Research Foundation Plan in Higher Education Institutions of Henan Province under Grant 20A520037.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhou, P., Chen, X., Liu, Z., Braud, T., Hui, P., Kangasharju, J.: DRLE: decentralized reinforcement learning at the edge for traffic light control in the IoV. IEEE Trans. Intell. Transp. Syst. 22(4), 2262–2273 (2020)

    Article  Google Scholar 

  2. Wu, C., Liu, Z., Liu, F., Yoshinaga, T., Ji, Y., Li, J.: Collaborative learning of communication routes in edge-enabled multi-access vehicular environment. IEEE Trans. Cogn. Commun. Netw. 6(4), 1155–1165 (2020)

    Article  Google Scholar 

  3. Llorca, D.F., Martínez, A.H., Daza, I.G.: Vision-based vehicle speed estimation for its: a survey. arXiv preprint arXiv:2101.06159 (2021)

  4. Luvizon, D.C., Nassu, B.T., Minetto, R.: A video-based system for vehicle speed measurement in urban roadways. IEEE Trans. Intell. Transp. Syst. 18(6), 1393–1404 (2016)

    Google Scholar 

  5. Famouri, M., Azimifar, Z., Wong, A.: A novel motion plane-based approach to vehicle speed estimation. IEEE Trans. Intell. Transp. Syst. 20(4), 1237–1246 (2018)

    Article  Google Scholar 

  6. Afifah, F., Nasrin, S., Mukit, A.: Vehicle speed estimation using image processing. J. Adv. Res. Appl. Mech. 48(1), 9–16 (2019)

    Google Scholar 

  7. Yang, L., Li, M., Song, X., Xiong, Z., Hou, C., Qu, B.: Vehicle speed measurement based on binocular stereovision system. IEEE Access 7, 106628–106641 (2019)

    Article  Google Scholar 

  8. El Bouziady, A., Thami, R.O.H., Ghogho, M., Bourja, O., El Fkihi, S.: Vehicle speed estimation using extracted surf features from stereo images. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1–6. IEEE (2018)

    Google Scholar 

  9. Jiang, J., Mi, C., Wu, M., Zhang, Z., Feng, Y.: Study on a real-time vehicle speed measuring method at highway toll station. In: 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI), pp. 1–5. IEEE (2019)

    Google Scholar 

  10. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  11. Girshick, R.: Fast r-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  12. Guo, C., Cui, Y., Liu, Z.: Optimal multicast of tiled 360 VR video. IEEE Wireless Commun. Lett. 8(1), 145–148 (2018)

    Article  Google Scholar 

  13. Liu, Z., Cheung, G., Chakareski, J., Ji, Y.: Multiple description coding and recovery of free viewpoint video for wireless multi-path streaming. IEEE J. Sel. Top. Sign. Process. 9(1), 151–164 (2014)

    Article  Google Scholar 

  14. Zhou, H., Wang, X., Liu, Z., Ji, Y., Yamada, S.: Resource allocation for svc streaming over cooperative vehicular networks. IEEE Trans. Veh. Technol. 67(9), 7924–7936 (2018)

    Article  Google Scholar 

  15. Liu, Z., et al.: Point cloud video streaming in 5G systems and beyond: challenges and solutions. In: IEEE Network (2021)

    Google Scholar 

  16. Babu, R.V., Tom, M., Wadekar, P.: A survey on compressed domain video analysis techniques. Multimedia Tools Appl. 75(2), 1043–1078 (2014). https://doi.org/10.1007/s11042-014-2345-z

    Article  Google Scholar 

  17. Jaballah, S., Larabi, M.C.: Fast object detection in H264/AVC and HEVC compressed domains for video surveillance. In: 2019 8th European Workshop on Visual Information Processing (EUVIP), pp. 123–128. IEEE (2019)

    Google Scholar 

  18. Zhao, L., He, Z., Cao, W., Zhao, D.: Real-time moving object segmentation and classification from HEVC compressed surveillance video. IEEE Trans. Circuits Syst. Video Technol. 28(6), 1346–1357 (2016)

    Article  Google Scholar 

  19. Zhang, B., Liu, Z., Chan, S.H.G., Cheung, G.: Collaborative wireless freeview video streaming with network coding. IEEE Trans. Multimedia 18(3), 521–536 (2016)

    Article  Google Scholar 

  20. Liu, Z., Zhan, C., Cui, Y., Wu, C., Hu, H.: Robust edge computing in UAV systems via scalable computing and cooperative computing. IEEE Wireless Commun. 28(5), 36–42 (2021)

    Article  Google Scholar 

  21. Poppe, C., De Bruyne, S., Paridaens, T., Lambert, P., Van de Walle, R.: Moving object detection in the H 264/AVC compressed domain for video surveillance applications. J. Vis. Commun. Image Represent. 20(6), 428–437 (2009)

    Article  Google Scholar 

  22. Ma, M., Song, H.: Effective moving object detection in H 264/AVC compressed domain for video surveillance. Multimedia Tools Appl. 78(24), 35195–35209 (2019)

    Article  Google Scholar 

  23. Chen, Y.M., Bajic, I.V.: A joint approach to global motion estimation and motion segmentation from a coarsely sampled motion vector field. IEEE Trans. Circuits Syst. Video Technol. 21(9), 1316–1328 (2011)

    Article  Google Scholar 

  24. Tomar, S.: Converting video formats with FFmpeg. Linux J. 2006(146), 10 (2006)

    Google Scholar 

  25. FFmpeg Developers: FFmpeg tool (Version 4.4) [Software] (2021). http://ffmpeg.org/

  26. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  27. Zhu, Y., Cheng, S., Stanković, V., Stanković, L.: Image registration using BP-sift. J. Vis. Commun. Image Represent. 24(4), 448–457 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Yangjie Cao, Qi Wu, Bo Zhang & Junfeng Li

  2. The University of Electro-Communications, Chofu, Japan

    Zhi Liu

Authors
  1. Yangjie Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Zhang .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Spain

    Carlos T. Calafate

  2. VTT Technical Research Centre of Finland, Oulu, Finland

    Xianfu Chen

  3. University of Macau, Taipa, China

    Yuan Wu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, Y., Wu, Q., Zhang, B., Liu, Z., Li, J. (2022). FSE-MV: Compressed Domain Video Information Assisted Hybrid Real-Time Vehicle Speed Estimation. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94763-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94762-0

  • Online ISBN: 978-3-030-94763-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation