Skip to main content
SpringerLink
Log in

Video Target Tracking Based on Adaptive Kalman Filter

  • Conference paper
  • First Online:
Communications, Signal Processing, and Systems (CSPS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

Abstract

Video tracking technology is a hot topic in computer vision research. Video tracking technology is widely used, such as robot vision, intelligent traffic management, medical diagnosis and intelligent monitoring. Therefore, it is of theoretical significance and practical value to study video target tracking technology. In this paper, the background subtraction method and adaptive Kalman filter are combined to realize real time video target tracking. The experimental results show that the proposed method can improve the tracking accuracy.

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 629.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 799.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 799.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Sriharsha KV, Rao NV (2015) Dynamic scene analysis using Kalman filter and mean shift tracking algorithms. In: 2015 6th international conference on computing, communication and networking technologies (ICCCNT)

    Google Scholar 

  2. Akhlaghi S, Zhou N, Huang Z (2017) Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation. In: 2017 IEEE power & energy society general meeting

    Google Scholar 

  3. Fuad AG, Anazida Z (2017) Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter. Pervasive Mobile Comput 40:139–155

    Article  Google Scholar 

  4. Kim ZW(2008) Real time object tracking based on dynamic feature grouping with background subtraction. In: IEEE conference on computer vision & pattern recognition

    Google Scholar 

  5. Rudolph EK (1960) A new approach to linear filtering and prediction problems. Trans ASME-J Basic Eng 82(Series D):35–45

    Google Scholar 

  6. Congshan QU, Hualong XU, Tan Y(2008) SINS/CNS integrated navigation solution using adaptive unscented Kalman filtering. In: International Conference on Modelling

    Google Scholar 

  7. Emami M, Taban MR (2018) A novel intelligent adaptive Kalman filter for estimating the submarine’s velocity: with experimental evaluation. Ocean Eng 158:401–403

    Article  Google Scholar 

  8. Sarala BV, Swathi BV (2017) Object tracking using block motion estimation with adaptive Kalman estimates. In: 2017 2nd Ieee international conference on recent trends in electronics, information & communication technology (RTEICT)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61501176, Natural Science Foundation of Heilongjiang Province F2018025, University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province UNPYSCT-2016017, and the postdoctoral scientific research developmental fund of Heilongjiang Province in 2017 LBH-Q17149.

Author information

Authors and Affiliations

  1. College of Electronic Engineering, Heilongjiang University, Harbin, 150080, China

    Futong He, Jiaqi Zhen & Zhifang Wang

Authors
  1. Futong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaqi Zhen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhifang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiaqi Zhen .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

He, F., Zhen, J., Wang, Z. (2020). Video Target Tracking Based on Adaptive Kalman Filter. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_141

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9409-6_141

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation