Skip to main content
SpringerLink
Log in

Robust object tracking with crow search optimized multi-cue particle filter

  • Industrial and commercial application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon crow search optimization to overcome low performing particles detected as the outlier. Proposed outlier detection mechanism with transductive reliability achieves faster convergence of the proposed PF tracking framework. In addition, we present an adaptive fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using the proposed fusion model not only enhance the performance of the resampling method but also achieve optimal state estimation. Performance of the proposed tracker has been evaluated over benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveal that the proposed tracker not only outperforms existing solutions but also efficiently handles various tracking challenges. On average of the outcome, we achieve CLE of 10.99 and F measure of 0.683.

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

Similar content being viewed by others

References

  1. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: 2006 IEEE CS conference on computer vision and pattern recognition, vol 1, pp 798–805

  2. Ahmadi K, Salari E (2016) Social-spider optimised particle filtering for tracking of targets with discontinuous measurement data. IET Comput Vis 11(3):246–254

    Google Scholar 

  3. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  4. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  5. Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632

    Article  Google Scholar 

  6. Bai L, Wang Y, Fairhurst M (2010) Multiple filters for road detection and tracking. Pattern Anal Appl 13(3):251–262

    Article  MathSciNet  Google Scholar 

  7. Bai Y, Wang D (2006) Fundamentals of fuzzy logic control fuzzy sets, fuzzy rules and defuzzifications. In: Wang D, Bai Y, Zhuang H (eds) Advanced fuzzy logic technologies in industrial applications. Springer, Berlin, pp 17–36

    Chapter  Google Scholar 

  8. Bhateja A, Walia GS, Kapoor R (2016) Non linear state estimation using PF based on backtracking search optimization. In: International conference on computer, communication and automation, pp 342–347

  9. Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35:99–109

    MathSciNet  MATH  Google Scholar 

  10. Bolić M, Djurić PM, Hong S (2004) Resampling algorithms for particle filters: a computational complexity perspective. EURASIP J Adv Signal Process 2004(15):403686

    Article  MathSciNet  Google Scholar 

  11. Brasnett P, Mihaylova L, Bull D, Canagarajah N (2007) Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image Vis Comput 25(8):1217–1227

    Article  Google Scholar 

  12. Choe G, Wang T, Liu F, Hyon S, Ha J (2014) Particle filter with spline resampling and global transition model. IET Comput Vis 9(2):184–197

    Article  Google Scholar 

  13. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577

    Article  Google Scholar 

  14. Gao ML, Li LL, Sun XM, Yin LJ, Li HT, Luo DS (2015) Firefly algorithm based particle filter method for visual tracking. Optik Int J Light Electron Opt 126:1705–1711

    Article  Google Scholar 

  15. Gordon N, Ristic B, Arulampalam S (2003) Beyond the Kalman filter: particle filters for tracking applications, vol 3. Artech House, London, pp 1077–2626

    MATH  Google Scholar 

  16. Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE proceedings F (radar and signal processing), vol 140, pp 107–113. IET

  17. Han H, Ding YS, Hao KR, Liang X (2011) An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking. Comput Math Appl 62:2685–2695

    Article  MathSciNet  Google Scholar 

  18. Isard M, Blake A (1998) Condensation–conditional density propagation for visual tracking. Int J Comput Vis 29:5–28

    Article  Google Scholar 

  19. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. In: IEEE conference on computer vision and pattern recognition, pp 1822–1829

  20. Khasnabish N, Detroja KP A (2018) Stochastic resampling based selective particle filter for visual object tracking. In: Indian control conference (ICC) 2018. IEEE, pp 42–47

  21. Lazarevic-McManus N, Renno J, Makris D, Jones GA (2008) An object-based comparative methodology for motion detection based on the \(f\) measure. Comput Vis Image Underst 111:74–85

    Article  Google Scholar 

  22. Li T, Sun S, Sattar TP, Corchado JM (2014) Fight sample degeneracy and impoverishment in particle filters: a review of intelligent approaches. Expert Syst Appl 41(8):3944–3954

    Article  Google Scholar 

  23. Murphy RR (1996) Biological and cognitive foundations of intelligent sensor fusion. IEEE Trans Syst Man Cybern Part A Syst Hum 26(1):42–51

    Article  Google Scholar 

  24. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  25. Rohilla R, Sikri V, Kapoor R (2016) Spider monkey optimisation assisted PF for robust object tracking. IET Comput Vis 11(3):207–219

    Article  Google Scholar 

  26. Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141

    Article  Google Scholar 

  27. Sardari F, Moghaddam ME (2016) An object tracking method using modified galaxy-based search algorithm. Swarm Evolut Comput 30:27–38

    Article  Google Scholar 

  28. Sardari F, Moghaddam ME (2017) A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm. Appl Soft Comput 50:280–299

    Article  Google Scholar 

  29. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE CS conference on computer vision and pattern recognition, vol 2, pp 246–252

  30. Walia GS, Kapoor R (2014) Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search. Expert Syst Appl 41(14):6315–6326

    Article  Google Scholar 

  31. Walia GS, Kapoor R (2016) Recent advances on multicue object tracking: a survey. Artif Intell Rev 46:1–39

    Article  Google Scholar 

  32. Walia GS, Kapoor R (2016) Robust object tracking based upon adaptive multi-cue integration for video surveillance. Multimed Tools Appl 75(23):15821–15847

    Article  Google Scholar 

  33. Walia GS, Raza S, Gupta A, Asthana R, Singh K (2017) A novel approach of multi-stage tracking for precise localization of target in video sequences. Expert Syst Appl 78:208–224

    Article  Google Scholar 

  34. Wang D, Lu H, Xiao Z, Yw Chen (2013) Fast and effective color-based object tracking by boosted color distribution. Pattern Anal Appl 16(4):647–661

    Article  MathSciNet  Google Scholar 

  35. Wang Z, Liu Z, Liu W, Kong Y (2011) Particle filter algorithm based on adaptive resampling strategy. In: 2011 international conference on electronic and mechanical engineering and information technology (EMEIT), vol 6, pp 3138–3141

  36. Weng SK, Kuo CM, Tu SK (2006) Video object tracking using adaptive Kalman filter. J Vis Commun Image Represent 17(6):1190–1208

    Article  Google Scholar 

  37. Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition, pp 2411–2418

  38. Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46:397–411

    Article  Google Scholar 

  39. Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. In: European conference on computer vision. Springer, pp 864–877

  40. Zhao J, Li Z (2010) Particle filter based on particle swarm optimization resampling for vision tracking. Expert Syst Appl 37:8910–8914

    Article  Google Scholar 

  41. Zhou H, Deng Z, Xia Y, Fu M (2016) A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing 216:208–215

    Article  Google Scholar 

  42. Zuo J (2013) Dynamic resampling for alleviating sample impoverishment of particle filter. IET Radar Sonar Navig 7:968–977

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Defence Research and Development Organization, Delhi, India

    Gurjit Singh Walia

  2. Delhi Technological University, Delhi, India

    Ashish Kumar & Kapil Sharma

  3. Netaji Subhas Institute of Technology, Delhi, India

    Astitwa Saxena

  4. Malaviya National Institute of Technology, Jaipur, India

    Kuldeep Singh

Authors
  1. Gurjit Singh Walia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Astitwa Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kapil Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kuldeep Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kapil Sharma.

Ethics declarations

Conflict of interest

All authors declare that they have no conflicts 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

Walia, G.S., Kumar, A., Saxena, A. et al. Robust object tracking with crow search optimized multi-cue particle filter. Pattern Anal Applic 23, 1439–1455 (2020). https://doi.org/10.1007/s10044-019-00847-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-019-00847-7

Keywords

Search

Navigation