Skip to main content
SpringerLink
Log in

Moving target recognition and tracking algorithm based on multi-source information perception

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper proposed a non- monitoring video multi- object tracking algorithm based on fast resampling particle wave filtering in order to improve the unsupervised monitoring effect of video moving objects. Each particle (sample) can represent the real state assumption. In each time step, the likelihood function is used to evaluate and quantify each particle. The estimated state is approximated by the average value of all the particles after evaluating each particle. To avoid degradation, we use particle resampling to create a new weighing set. Finally, by the simulation test of a real monitoring image show that the proposed algorithm improves the tracking accuracy by more than 20% and the computational efficiency by more than 30% compared with the contrast algorithm, which verifies the effectiveness of the proposed method.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Achary UR, YukiHagiwara SN, Deshpande SS, Koh JEW, Shu Lih O, Arunkumar N, Ciaccio EJ, Lim CM (2019) Characterization of focal EEG signals: a review. Futur Gener Comput Syst 91:290–299

    Article  Google Scholar 

  2. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency[J]. Ann Stat 29(4):1165–1188

    Article  MathSciNet  Google Scholar 

  3. Benko M, Härdle W, Kneip A (2009) Common functional principal components[J]. Ann Stat 37(1):1–34

    Article  MathSciNet  Google Scholar 

  4. Dongdong J, Arunkumar N, Wenyu Z, Beibei L, Xinlei Z, Guangjian Z (2019) Semantic clustering fuzzy c means spectral model based comparative analysis of cardiac color ultrasound and electrocardiogram in patients with left ventricular heart failure and cardiomyopathy. Futur Gener Comput Syst 92:324–328

    Article  Google Scholar 

  5. Elamaran V, Arunkumar N, Hussein AF, Solarte M, Ramirez-Gonzalez G (2018) Spectral fault recovery analysis revisited with normal and abnormal heart sound signals. IEEE Access 6:62874–62879

    Article  Google Scholar 

  6. Haoyu L, Jianxing L, Arunkumar N, Hussein AF, Jaber MM (2018) An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.12.001

  7. Jiajie L, Narasimhan K, Elamaran V, Arunkumar N, Solarte M, Ramirez-Gonzalez G (2018) Clinical decision support system for alcoholism detection using the analysis of EEG signals. IEEE Access 6:61457–61461

    Article  Google Scholar 

  8. Khamparia A, Singh A, Anand D et al (2018) A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Comput & Applic. https://doi.org/10.1007/s00521-018-3896-0

  9. Khan JA, Van Aelst S, Zamar RH (2010) Fast robust estimation of prediction error based on resampling[J]. Computational Statistics & Data Analysis 54(12):3121–3130

    Article  MathSciNet  Google Scholar 

  10. Li R (2013) An object tracking algorithm based on global SURF feature[J]. Journal of Information & Computational Science 10(7):2159–2167

    Article  Google Scholar 

  11. Medeiros H, Holguín G, Shin PJ et al (2010) A parallel histogram-based particle filter for object tracking on SIMD-based smart cameras[J]. Comput Vis Image Underst 114(11):1264–1272

    Article  Google Scholar 

  12. Mihaylova L, Angelova D, Honary S et al (2007) Mobility tracking in cellular networks using particle filtering[J]. IEEE Trans Wirel Commun 6(10):3589–3599

    Article  Google Scholar 

  13. Nei M (1996) Phylogenetic analysis in molecular evolutionary genetics[J]. Annu Rev Genet 30(1):371–403

    Article  Google Scholar 

  14. Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput & Applic:1–7. https://doi.org/10.1007/s00521-018-3689-5

  15. Santamaria-Granados L, Munoz-Organero M, Ramirez-Gonzalez G, Abdulhay E, Arunkumar N (2018) Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS). IEEE Access. https://doi.org/10.1109/ACCESS.2018.2883213

  16. Sathishkumar BR, Sundaravadivazhagan B, Martin B, Sasi G, Chandrasekar M, Kumar SR, … Arunkumar N Revisiting computer networking protocols by wireless sniffing on brain signal/image portals. Neural Comput & Applic:1–13. https://doi.org/10.1007/s00521-018-3919-x

  17. Tsingos N, Gallo E, Drettakis G (2004) Perceptual audio rendering of complex virtual environments[J]. ACM Trans Graph (TOG) 23(3):249–258

    Article  Google Scholar 

  18. Turechek WW, Madden L (1999) Spatial pattern analysis of strawberry leaf blight in perennial production systems.[J]. Phytopathology 89(5):421–433

    Article  Google Scholar 

  19. Wu Z, Wang H, Arunkumar N (2019) Bayesian analysis model for the use of anesthetic analgesic drugs in cancer patients based on geometry reconstruction. Futur Gener Comput Syst 93:170–175

    Article  Google Scholar 

  20. Yuan M, Kendziorski C, Li H et al (2006) Hidden Markov models for microarray time course data in multiple biological conditions [with discussions, rejoinder][J]. Publ Am Stat Assoc 101(476):1323–1332

    Article  Google Scholar 

  21. Zhao H, Duan X, Cao Y (2007) Color-based object tracking using fast survival-of-the-fittest method[C]. In: International conference on fuzzy systems and knowledge discovery. IEEE, pp 311–316

Download references

Acknowledgements

Anhui province outstanding young talents support program (gxyq2017157, gxyq2017159); Anhui province major teaching reform project(2016jyxm0777); Anhui natural science research project (KJ2017A838, KJ2017A837, KJ2018A0669, KJ2018A0670).

Author information

Authors and Affiliations

  1. College of Information Engineering, Fuyang Normal University, Fuyang, 236041, China

    Yingying Feng, Hui Liu & Shasha Zhao

Authors
  1. Yingying Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shasha Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Liu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Liu, H. & Zhao, S. Moving target recognition and tracking algorithm based on multi-source information perception. Multimed Tools Appl 79, 16941–16954 (2020). https://doi.org/10.1007/s11042-019-7483-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7483-x

Keywords

Search

Navigation