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

  • Gurjit Singh Walia
  • Ashish Kumar
  • Astitwa Saxena
  • Kapil SharmaEmail author
  • Kuldeep Singh
Industrial and commercial application


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.


Particle filter CSA Object tracking Fusion model 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.


  1. 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 798–805Google Scholar
  2. 2.
    Ahmadi K, Salari E (2016) Social-spider optimised particle filtering for tracking of targets with discontinuous measurement data. IET Comput Vis 11(3):246–254CrossRefGoogle Scholar
  3. 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):174–188CrossRefGoogle Scholar
  4. 4.
    Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12CrossRefGoogle Scholar
  5. 5.
    Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  6. 6.
    Bai L, Wang Y, Fairhurst M (2010) Multiple filters for road detection and tracking. Pattern Anal Appl 13(3):251–262MathSciNetCrossRefGoogle Scholar
  7. 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 17–36CrossRefzbMATHGoogle Scholar
  8. 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 342–347Google Scholar
  9. 9.
    Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35:99–109MathSciNetzbMATHGoogle Scholar
  10. 10.
    Bolić M, Djurić PM, Hong S (2004) Resampling algorithms for particle filters: a computational complexity perspective. EURASIP J Adv Signal Process 2004(15):403686MathSciNetzbMATHCrossRefGoogle Scholar
  11. 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):1217–1227CrossRefGoogle Scholar
  12. 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):184–197CrossRefGoogle Scholar
  13. 13.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577CrossRefGoogle Scholar
  14. 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 126:1705–1711CrossRefGoogle Scholar
  15. 15.
    Gordon N, Ristic B, Arulampalam S (2003) Beyond the Kalman filter: particle filters for tracking applications, vol 3. Artech House, London, pp 1077–2626zbMATHGoogle Scholar
  16. 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. IETGoogle Scholar
  17. 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 62:2685–2695MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Isard M, Blake A (1998) Condensation–conditional density propagation for visual tracking. Int J Comput Vis 29:5–28CrossRefGoogle Scholar
  19. 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 1822–1829Google Scholar
  20. 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 42–47Google Scholar
  21. 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 111:74–85CrossRefGoogle Scholar
  22. 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):3944–3954CrossRefGoogle Scholar
  23. 23.
    Murphy RR (1996) Biological and cognitive foundations of intelligent sensor fusion. IEEE Trans Syst Man Cybern Part A Syst Hum 26(1):42–51CrossRefGoogle Scholar
  24. 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):971–987zbMATHCrossRefGoogle Scholar
  25. 25.
    Rohilla R, Sikri V, Kapoor R (2016) Spider monkey optimisation assisted PF for robust object tracking. IET Comput Vis 11(3):207–219CrossRefGoogle Scholar
  26. 26.
    Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141CrossRefGoogle Scholar
  27. 27.
    Sardari F, Moghaddam ME (2016) An object tracking method using modified galaxy-based search algorithm. Swarm Evolut Comput 30:27–38CrossRefGoogle Scholar
  28. 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 50:280–299CrossRefGoogle Scholar
  29. 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 246–252Google Scholar
  30. 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):6315–6326CrossRefGoogle Scholar
  31. 31.
    Walia GS, Kapoor R (2016) Recent advances on multicue object tracking: a survey. Artif Intell Rev 46:1–39CrossRefGoogle Scholar
  32. 32.
    Walia GS, Kapoor R (2016) Robust object tracking based upon adaptive multi-cue integration for video surveillance. Multimed Tools Appl 75(23):15821–15847CrossRefGoogle Scholar
  33. 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 78:208–224CrossRefGoogle Scholar
  34. 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):647–661MathSciNetCrossRefGoogle Scholar
  35. 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 3138–3141Google Scholar
  36. 36.
    Weng SK, Kuo CM, Tu SK (2006) Video object tracking using adaptive Kalman filter. J Vis Commun Image Represent 17(6):1190–1208CrossRefGoogle Scholar
  37. 37.
    Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: IEEE conference on computer vision and pattern recognition, pp 2411–2418Google Scholar
  38. 38.
    Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46:397–411zbMATHCrossRefGoogle Scholar
  39. 39.
    Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. In: European conference on computer vision. Springer, pp 864–877Google Scholar
  40. 40.
    Zhao J, Li Z (2010) Particle filter based on particle swarm optimization resampling for vision tracking. Expert Syst Appl 37:8910–8914CrossRefGoogle Scholar
  41. 41.
    Zhou H, Deng Z, Xia Y, Fu M (2016) A new sampling method in particle filter based on Pearson correlation coefficient. Neurocomputing 216:208–215CrossRefGoogle Scholar
  42. 42.
    Zuo J (2013) Dynamic resampling for alleviating sample impoverishment of particle filter. IET Radar Sonar Navig 7:968–977CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Gurjit Singh Walia
    • 1
  • Ashish Kumar
    • 2
  • Astitwa Saxena
    • 3
  • Kapil Sharma
    • 2
    Email author
  • Kuldeep Singh
    • 4
  1. 1.Defence Research and Development OrganizationDelhiIndia
  2. 2.Delhi Technological UniversityDelhiIndia
  3. 3.Netaji Subhas Institute of TechnologyDelhiIndia
  4. 4.Malaviya National Institute of TechnologyJaipurIndia

Personalised recommendations