Skip to main content
SpringerLink
Log in

Robust visual tracking based on adaptive gradient descent optimization of a cost function with parametric models of appearance and geometry

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

Abstract

In the field of visual tracking, there are many issues to consider which make the development of a robust tracking method very difficult, among these complications; the appearance change of the target, the fast motion, the background clutter, the camera motion, scale variation and the in plane Rotation. To override these problems, we develop an effective general framework for object tracking that addresses most of these issues. First the tracking problem is formulated in the form of a robust cost function which is a composition of the appearance and dynamic model, this formulation ensures the integration of the appearance and motion informations. Second the minimization is accomplished by the gradient descent optimization with adaptive step size prediction, the step size adaptation accelerates the optimization process and increases the accuracy. Throughout, we present experimental results made on different challenging sequences, the experimentations results demonstrate the efficiency and effectiveness of our methods.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Notes

  1. http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html

  2. http://votchallenge.net/vot2016/trackers.html

References

  1. Ahmad A, Abdul J, Jianwei N, Xiaoke Z, Saima R, Javed A, Muhammad AI (2016) Visual object tracking—classical and contemporary approaches. Frontiers of Computer Science 10(1):167–188

    Article  Google Scholar 

  2. Avidan S (2007) Ensemble tracking. IEEE Trans Pattern Anal Mach Intell 29(2):261–271

    Article  Google Scholar 

  3. Choe G, Wang T, Liu F, Choe C, Jong M (2015) An advanced association of particle filtering and kernel based object tracking. Multimed Tools Appl 74(18):7595–7619

    Article  Google Scholar 

  4. Coifman B, Beymer D, McLauchlan P (1998) A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies 6(4):271–288

    Article  Google Scholar 

  5. Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643

    Article  Google Scholar 

  6. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(4):996–1002

    Article  Google Scholar 

  7. Dhassi Y, Aarab A (2018) Visual tracking based on adaptive interacting multiple model particle filter by fusing multiples cues. Multimed Tools Appl:1–34

  8. Iswanto IA, Li B (2017) Visual object tracking based on mean-shift and particle-Kalman filter. Procedia Computer Science 116:587–595

    Article  Google Scholar 

  9. Jeong J, Yoon TS, Park JB (2017) Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking. Expert Syst Appl 79:194–206

    Article  Google Scholar 

  10. Karasulu B, Korukoglu S (2011) A software for performance evaluation and comparison of people detection and tracking methods in video processing. Multimed Tools Appl 55(3):677–723

    Article  Google Scholar 

  11. Karavasilis V, Nikou C, Likas A (2015) Visual tracking using spatially weighted likelihood of Gaussian mixtures. Comput Vis Image Underst 000:1–15

    Google Scholar 

  12. Klein S, Pluim J, Staring M, Viergever M (2009) Adaptive stochastic gradient descent optimisation for image registration. Int J Comput Vis 81(3):227–239

    Article  Google Scholar 

  13. Kong J, Liu C, Jiang M, Wu J, Tian S, Lai H (2016) Generalized ℓP-regularized representation for visual tracking. Neurocomputing 213:155–161

    Article  Google Scholar 

  14. Leichter I, Lindenbaum M, Rivlin E (2010) Mean shift tracking with multiple reference color histograms. Comput Vis Image Underst 114:400–408

    Article  Google Scholar 

  15. Li G, Liang D, Huang Q, Jiang S, Gao W (2008) Object tracking using incremental 2D-LDA learning and Bayes inference. In: Image Processing. ICIP 2008. 15th IEEE International Conference on

  16. Li X, Hu W, Shen C et al (2013) A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology 4(4):58

    Article  Google Scholar 

  17. Li P, Wang D, Wang L, Lu H (2018) Deep visual tracking: review and experimental comparison. Pattern Recogn 76:323–338

    Article  Google Scholar 

  18. Lin SD, Lin J-J, Chuang C-Y (2015) Particle filter with occlusion handling for visual tracking. IET Image Process 9(11):959–968

    Article  MathSciNet  Google Scholar 

  19. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: 21st international conference on pattern recognition (ICPR 2012), Tsukuba, Japan

  20. Liu Z, Song Y-q, Xie C-h, Tang Z (2016) A new clustering method of gene expression data based on multivariate Gaussian mixture models. SIViP 10(2):359–368

    Article  Google Scholar 

  21. Pan Z, Liu S, Sangaiah AK, Muhammad K (2018) Visual attention feature (VAF) : a novel strategy for visual tracking based on cloud platform in intelligent surveillance systems. Journal of Parallel and Distributed Computing 120:182–194

    Article  Google Scholar 

  22. Shi Y, Zhao Y, Deng N, Yang K (2015) The augmented Lagrange multiplier for robust visual tracking withsparse representation. Optik 126:937–941

    Article  Google Scholar 

  23. Smeulders AWM, Chu DM, Cucchiara R (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468

    Article  Google Scholar 

  24. van Mourik MJW, Zaar DVJ, Smulders MW, Heijman J, Lumens J, Dokter JE, Passos VL, Schalla S, Knackstedt C, Schummers G, Gjesdal O, Edvardsen T, Bekkers SCAM (2018) Adding speckle-tracking echocardiography to visual assessment of Systolic Wall motion abnormalities improves the detection of myocardial infarction. J Am Soc Echocardiogr 32:65–73

    Article  Google Scholar 

  25. Villagra J, Acosta L, Artuñedo A, Blanco R, Clavijo M, Fernández C, Godoy J, Haber R, Jiménez F, Martínez C, Naranjo JE, Navarro PJ, Paúl A, Sánchez F (2018) Automated driving. In: Intelligent vehicles, enabling technologies and future developments, pp 275–342

  26. Vysochanskij DF, Petunin YI (1980) Justification of the 3σ rule for unimodal distributions. Theory of Probability and Mathematical Statistics 21:25–36

    MATH  Google Scholar 

  27. Wang H (2015) Adaptive visual tracking for robotic systems without image-space velocity measurement. Automatica 55:294–301

    Article  MathSciNet  MATH  Google Scholar 

  28. Yang W, Zhao M, Huang Y, Zheng Y (2018) Adaptive online learning based robust visual tracking. IEEE Access 6:14790–14798

    Article  Google Scholar 

  29. Ye L, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012)

  30. Yu W, Hou Z, Hu D, Wang P (2017) Robust mean shift tracking based on refined appearance model and online update. Multimed Tools Appl 76(8):10973–10990

    Article  Google Scholar 

  31. Zhi-Qiang H, Xiang L, Wang Sheng Y, Wu L, An Qi H (2014) Mean-shift tracking algorithm with improved background-weighted histogram. In: Intelligent systems design and engineering applications (ISDEA)

  32. Zhou Z, Zhou M, Li J (2017) Object tracking method based on hybrid particle filter and sparse representation. Multimed Tools Appl 76(2):2979–2993

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Electronics, Signals, Systems and Computers, Department Of Physics Faculty of Sciences Dhar- Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco

    Younes Dhassi & Abdellah Aarab

Authors
  1. Younes Dhassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdellah Aarab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Younes Dhassi.

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

Dhassi, Y., Aarab, A. Robust visual tracking based on adaptive gradient descent optimization of a cost function with parametric models of appearance and geometry. Multimed Tools Appl 78, 21349–21373 (2019). https://doi.org/10.1007/s11042-019-7386-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7386-x

Keywords

Search

Navigation