Skip to main content

Advertisement

SpringerLink
Log in

A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, Grasshopper Optimization Algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases.

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

Similar content being viewed by others

References

  1. John Holland H. (1992) Genetic algorithms, Sci Am a Div Nat Am Inc 267:1, pp 66–73

  2. Dorigo M, Stützle T (2009) Ant colony optimization. Encycl Mach Learn 1(May):28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  MATH  Google Scholar 

  3. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  4. Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. Proc Int Symp Micro Mach Hum Sci. pp 39–43. doi: https://doi.org/10.1109/mhs.1995.494215.

  5. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  6. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015

    Article  Google Scholar 

  7. Venkata Rao R (2016) Review of applications of tlbo algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis Sci Lett. 5(1):1–30. https://doi.org/10.5267/j.dsl.2015.9.003

    Article  MathSciNet  Google Scholar 

  8. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  9. Rao RV (2016) Jaya : A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34. https://doi.org/10.5267/j.ijiec.2015.8.004

    Article  Google Scholar 

  10. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  11. Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031

    Article  Google Scholar 

  12. Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30. https://doi.org/10.1016/j.swevo.2016.01.004

    Article  Google Scholar 

  13. Squillero G, Tonda A (2016) Divergence of character and premature convergence: a survey of methodologies for promoting diversity in evolutionary optimization. Inf Sci (Ny) 329:782–799. https://doi.org/10.1016/j.ins.2015.09.056

    Article  Google Scholar 

  14. Zhang C, Chen J, Xin B (2013) Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning. Appl Soft Comput J 13(5):2947–2959. https://doi.org/10.1016/j.asoc.2012.02.028

    Article  Google Scholar 

  15. Zhang C, Ning J, Lu S, Ouyang D, Ding T (2009) A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization. Oper Res Lett 37(2):117–122. https://doi.org/10.1016/j.orl.2008.12.008

    Article  MathSciNet  MATH  Google Scholar 

  16. Basak A, Maity D, Das S (2013) A differential invasive weed optimization algorithm for improved global numerical optimization. Appl Math Comput 219(12):6645–6668. https://doi.org/10.1016/j.amc.2012.12.057

    Article  MathSciNet  MATH  Google Scholar 

  17. Lin Q et al (2017) A local search enhanced differential evolutionary algorithm for sparse recovery. Appl Soft Comput J 57:144–163. https://doi.org/10.1016/j.asoc.2017.03.034

    Article  Google Scholar 

  18. Liang JJ, Qu BY, Mao XB, Niu B, Wang DY (2014) Differential evolution based on fitness Euclidean-distance ratio for multimodal optimization. Neurocomput 137:252–260. https://doi.org/10.1016/j.neucom.2013.03.069

    Article  Google Scholar 

  19. Zheng YJ, Xu XL, Ling HF, Chen SY (2015) A hybrid fireworks optimization method with differential evolution operators. Neurocomput 148:75–82. https://doi.org/10.1016/j.neucom.2012.08.075

    Article  Google Scholar 

  20. Wang Q, Teng Z, Xing J, Gao J, Hu W, Maybank S (2018) Learning attentions: residual attentional siamese network for high performance online visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 4854–4863. doi: https://doi.org/10.1109/CVPR.2018.00510.

  21. Li B, Yan J, Wu W, Zhu Z, Hu X (2018) High performance visual tracking with siamese region proposal network. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 8971–8980. doi: https://doi.org/10.1109/CVPR.2018.00935.

  22. Zhang Z, Peng H (2019) Deeper and wider siamese networks for real-time visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 4586–4595. doi: https://doi.org/10.1109/CVPR.2019.00472.

  23. Fan H, Ling H (2019) Siamese cascaded region proposal networks for real-time visual tracking. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. pp 7944–7953. doi: https://doi.org/10.1109/CVPR.2019.00814.

  24. Zhu Z, Wang Q, Li B, Wu W (2018) Distractor-aware siamese networks for visual object tracking (arXiv:1808.06048v1 [csCV]). Eccv pp 1–17

  25. Zhang Y, Wang L, Qi J, Wang D, Feng M, Lu H (2018) Structured siamese network for real-time visual tracking. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinf) 11213:355–370. https://doi.org/10.1007/978-3-030-01240-3_22

    Article  Google Scholar 

  26. Li B, Wu W, Wang Q, Zhang F, Xing J, Yan J (2019) SIAMRPN++ Evolution of siamese visual tracking with very deep networks. In: Proceedings of the ieee conference on computer vision and pattern recognition, pp 4282–4291

  27. Bertinetto L, Valmadre J, Henriques JF, Vedaldi A, Torr PHS (2016) Fully-convolutional siamese networks for object tracking. Lect Not Comput Sci (including Subser Lect Not Artif Intell Lect Not Bioinf) 9914:850–865. https://doi.org/10.1007/978-3-319-48881-3_56

    Article  Google Scholar 

  28. Held D, Thrun S, Savarese S (2016) Learning to track at 100 FPS with deep regression networks . Lect Not Comput Sci Subser Lect Not Artif Intell Lect Not Bioinf 9905:749–765. https://doi.org/10.1007/978-3-319-46448-0_45

    Article  Google Scholar 

  29. Tao R, Gavves E, Smeulders AWM (2016) Siamese instance search for tracking . Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 10:1420–1429. https://doi.org/10.1109/CVPR.2016.158

    Article  Google Scholar 

  30. Bhat G, Danelljan M, Van Gool L, Timofte R (2019) Learning discriminative model prediction for tracking. Proc. IEEE Int Conf Comput Vis 6181–6190. doi: https://doi.org/10.1109/ICCV.2019.00628.

  31. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417. https://doi.org/10.1109/TEVC.2008.927706

    Article  Google Scholar 

  32. Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Evol Comput IEEE Trans 10(6):646–657. https://doi.org/10.1109/TEVC.2006.872133

    Article  Google Scholar 

  33. Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958. https://doi.org/10.1109/TEVC.2009.2014613

    Article  Google Scholar 

  34. Gao ML, Li LL, Sun XM, Yin LJ, Li HT, Luo DS (2015) Firefly algorithm (FA) based particle filter method for visual tracking. Optik (Stuttg) 126(18):1705–1711. https://doi.org/10.1016/j.ijleo.2015.05.028

    Article  Google Scholar 

  35. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071. https://doi.org/10.1007/s10489-018-1190-6

    Article  Google Scholar 

  36. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466. https://doi.org/10.1016/j.jocs.2017.07.018

    Article  Google Scholar 

  37. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32(16):12381–12401. https://doi.org/10.1007/s00521-020-04839-1

    Article  Google Scholar 

  38. Nenavath H, Jatoth RK (2018) A novel object tracking method using binary bat algorithm. J Eng Appl Sci 13(4):3817–3825. https://doi.org/10.3923/jeasci.2018.3817.3825

    Article  Google Scholar 

  39. Nenavath H, Jatoth RK (2018) A new method for ball tracking based on α-β, Linear Kalman and extended Kalman filters via bubble sort algorithm. Indones J Electr Eng Comput Sci 10(3):989–999. https://doi.org/10.11591/ijeecs.v10.i3.pp989-999

    Article  Google Scholar 

  40. Narayana M, Nenavath H, Chavan S, Koteswara Rao L (2019) Intelligent visual object tracking with particle filter based on modified grey wolf optimizer. Optik Stuttg 193(3):162913. https://doi.org/10.1016/j.ijleo.2019.06.013

    Article  Google Scholar 

  41. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

    Article  Google Scholar 

  42. Parouha RP, Das KN (2016) A robust memory based hybrid differential evolution for continuous optimization problem. Knowledge-Based Syst 103:118–131. https://doi.org/10.1016/j.knosys.2016.04.004

    Article  Google Scholar 

  43. Nenavath H, Kumar Jatoth DR, Das DS (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43(1):1–30. https://doi.org/10.1016/j.swevo.2018.02.011

    Article  Google Scholar 

  44. Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput J 62:1019–1043. https://doi.org/10.1016/j.asoc.2017.09.039

    Article  Google Scholar 

  45. Nenavath H, Jatoth RK (2019) Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking. Neural Comput Appl 31(9):5497–5526. https://doi.org/10.1007/s00521-018-3376-6

    Article  Google Scholar 

  46. Niu J, Zhong W, Liang Y, Luo N, Qian F (2015) Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization. Knowledge-Based Syst 88:253–263. https://doi.org/10.1016/j.knosys.2015.07.027

    Article  Google Scholar 

  47. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

    Article  Google Scholar 

  48. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  49. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  50. Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. Stud Comput Intell 143(1):155–171. https://doi.org/10.1007/978-3-540-68830-3_6

    Article  Google Scholar 

  51. Deng C, Zhao B, Deng A, Hu R (2009) New differential evolution algorithm with a second enhanced mutation operator . Int Work Intell Syst Appl ISA 2009(50705039):1–4. https://doi.org/10.1109/IWISA.2009.5072971

    Article  Google Scholar 

  52. Weber M, Tirronen V, Neri F (2010) Scale factor inheritance mechanism in distributed differential evolution. Soft Comput 14(11):1187–1207. https://doi.org/10.1007/s00500-009-0510-5

    Article  Google Scholar 

  53. Zou D, Wu J, Gao L, Li S (2013) A modified differential evolution algorithm for unconstrained optimization problems. Neurocomput 120:469–481. https://doi.org/10.1016/j.neucom.2013.04.036

    Article  Google Scholar 

  54. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210. https://doi.org/10.1109/TEVC.2004.826074

    Article  Google Scholar 

  55. T. Peram, K. Veeramachaneni, C. K. Mohan (2003) Fitness-distance-ratio based particle swarm optimization. IEEE Swarm Intell Symp SIS 2003 Proc 2:174–181

  56. Vandenbergh Engelbrecht FAP (2004) A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput 8(3):225–239. https://doi.org/10.1109/TEVC.2004.826069

    Article  Google Scholar 

  57. Rao RV, Patel V (2012) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3(4):535–560. https://doi.org/10.5267/j.ijiec.2012.03.007

    Article  Google Scholar 

  58. Zou F, Wang L, Hei X, Chen D, Yang D (2014) Teaching-learning-based optimization with dynamic group strategy for global optimization. Inf Sci (Ny) 273:112–131. https://doi.org/10.1016/j.ins.2014.03.038

    Article  Google Scholar 

  59. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144. https://doi.org/10.1016/j.amc.2013.02.017

    Article  MathSciNet  MATH  Google Scholar 

  60. Chen D, Zou F, Lu R, Wang P (2017) Learning backtracking search optimisation algorithm and its application. Inf Sci (Ny) 376:71–94. https://doi.org/10.1016/j.ins.2016.10.002

    Article  Google Scholar 

  61. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  62. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  63. Wang J, Zhang W, Zhang J (2016) Cooperative differential evolution with multiple populations for multiobjective optimization. IEEE Trans Cybern 46(12):2848–2861. https://doi.org/10.1109/TCYB.2015.2490669

    Article  Google Scholar 

  64. Nenavath H, Kumar Jatoth DR, Das DS (2018) A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking . Swarm Evol Comput 43:1–30

    Article  Google Scholar 

  65. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577. https://doi.org/10.1109/TPAMI.2003.1195991

    Article  Google Scholar 

  66. Khan ZH, Gu IY-H, Backhouse AG (2011) Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans Circuits Syst Video Technol 21(1):74–87

    Article  Google Scholar 

  67. Thida M, Eng H, Monekosso DN, Remagnino P (2012) A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets. Appl Soft Comput J 13(6):1–12

    Google Scholar 

  68. Gao M-L et al (2016) A novel visual tracking method using bat algorithm. Neurocomput 177:612–619. https://doi.org/10.1016/j.neucom.2015.11.072

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE Department, Koneru Lakshmaiah Education Foundation (Deemed to be University), Research Scholar, Green Fields, Vaddeswaram Guntur District, Andhra Pradesh, 522502, Hyderabad, India

    K. Narsimha Reddy

  2. ECE Department, Koneru Lakshmaiah Education Foundation (Deemed to be University), Professor, Green Fields, Vaddeswaram Guntur District, Andhra Pradesh, 522502, Hyderabad, India

    Polaiah Bojja

Authors
  1. K. Narsimha Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Polaiah Bojja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Narsimha Reddy.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Reddy, K.N., Bojja, P. A novel method to solve visual tracking problem: hybrid algorithm of grasshopper optimization algorithm and differential evolution. Evol. Intel. 15, 785–822 (2022). https://doi.org/10.1007/s12065-021-00567-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00567-0

Keywords

Search

Navigation