Skip to main content
Springer Nature Link
Log in

Single-target visual tracking using color compression and spatially weighted generalized Gaussian mixture models

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Visual tracking is a challenging task in computer vision, which intends to estimate the motion state of the target of interest in subsequent video frames. In that context, it is well-known that the rapid movement and rotation of the target affects the tracking results. This article proposes a novel single-target tracking algorithm based on the spatially weighted generalized Gaussian mixture model framework. The clustering method for color compression in preprocessing is considered to modify the frames to make them have sharper distributions. Then, the mixture models are built to express the color statistical features of the aimed area and context. The segmentation weights obtained from the responsivity of the pixels in the candidate ellipse to the target and background will guide the update of the target position and size in a heuristic way. The adjustment of models will depend on the aspect ratio change of the bounding ellipse. The performance of the proposed approach is verified on public datasets and compared with other algorithms. The experimental results show that our method achieved more accurate and robust tracking.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

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 computer society conference on computer vision and pattern recognition (CVPR’06) IEEE vol. 1: pp. 798–805

  2. Ashok Kumar P, Maddala JB, Martin Sagayam K (2021) Enhanced facial emotion recognition by optimal descriptor selection with neural network. IETE J Res. https://doi.org/10.1080/03772063.2021.1902868

    Article  Google Scholar 

  3. Banerjee A, Halder A (2010) An efficient image compression algorithm for almost dual-color image based on k-means clustering, bit-map generation and rle. In: 2010 international conference on computer and communication technology (ICCCT), pp. 201–205. IEEE

  4. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEEpp. 2544–2550

  5. Bouguila N, Ziou D (2005) A probabilistic approach for shadows modeling and detection. In: Proceedings of the 2005 international conference on image processing, ICIP 2005,IEEEGenoa, Italy, September vol. 11-14, pp. 329–332

  6. Boukouvalas Z, Fu GS, Adalı T (2015) An efficient multivariate generalized gaussian distribution estimator: application to iva. In: 2015 49th annual conference on information sciences and systems (CISS) IEEEpp. 1–4

  7. Buyval A, Gabdullin A, Mustafin R, Shimchik I (2018) Realtime vehicle and pedestrian tracking for didi udacity self-driving car challenge. In: 2018 IEEE international conference on robotics and automation (ICRA) IEEE, pp. 2064–2069

  8. Channoufi I, Bourouis S, Bouguila N, Hamrouni K (2018) Color image segmentation with bounded generalized gaussian mixture model and feature selection. In: 2018 4th International conference on advanced technologies for signal and image processing (ATSIP), IEEE pp. 1–6

  9. Chen Z, Zhong B, Li G, Zhang S, Ji, R (2020) Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp. 6668–6677

  10. Chou CH, Liu KC (2008) Colour image compression based on the measure of just noticeable colour difference. IET Image Process 2(6):304–322

    Article  MathSciNet  Google Scholar 

  11. Comaniciu D, Meer P (2002) Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  12. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: Proceedings IEEE conference on computer vision and pattern recognition. CVPR 2000 (Cat. No. PR00662), IEEEvol. 2, pp. 142–149

  13. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  14. Cui Z, Xiao S, Feng J, Yan S (2016) Recurrently target-attending tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp. 1449–1458

  15. Dadi HS, Pillutla GKM, Makkena ML (2018) Face recognition and human tracking using gmm, hog and svm in surveillance videos. Ann Data Sci 5(2):157–179

    Article  Google Scholar 

  16. Danelljan M, Gool LV, Timofte R (2020) Probabilistic regression for visual tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7183–7192

  17. Deledalle CA, Parameswaran S, Nguyen TQ (2018) Image denoising with generalized gaussian mixture model patch priors. SIAM J Imaging Sci 11(4):2568–2609

    Article  MathSciNet  Google Scholar 

  18. Elguebaly T, Bouguila N (2011) A nonparametric bayesian approach for enhanced pedestrian detection and foreground segmentation. In: IEEE Conference on computer vision and pattern recognition, CVPR Workshops 2011, Colorado Springs, IEEE Computer Society, CO, USA, 20-25 June, 2011, pp. 21–26

  19. Epaillard E, Bouguila N (2016) Proportional data modeling with hidden markov models based on generalized dirichlet and beta-liouville mixtures applied to anomaly detection in public areas. Pattern Recognit 55:125–136

    Article  Google Scholar 

  20. Fang KT, Kotz S, Ng KW (2018) Symmetric multivariate and related distributions. Chapman and Hall/CRC, Boca Raton

    Book  Google Scholar 

  21. Fernandez-Sanjurjo M, Bosquet B, Mucientes M, Brea VM (2019) Real-time visual detection and tracking system for traffic monitoring. Eng Appl Artif Intell 85:410–420

    Article  Google Scholar 

  22. Fiaz M, Mahmood A, Javed S, Jung SK (2019) Handcrafted and deep trackers: recent visual object tracking approaches and trends. ACM Comput Surv(CSUR) 52(2):1–44

    Article  Google Scholar 

  23. Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396

    Article  Google Scholar 

  24. Hao L, Shu-kui Z (2019) Moving object tracking algorithm based on improved gaussian mixture model. In: 2019 3rd International conference on electronic information technology and computer engineering (EITCE), IEEEpp. 271–275

  25. Hassan W, Bangalore N, Birch P, Young R, Chatwin C (2012) An adaptive sample count particle filter. Comput Vis Image Underst 116(12):1208–1222

    Article  Google Scholar 

  26. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: European conference on computer vision, Springerpp. 702–715

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

  28. Karavasilis V, Nikou C, Likas A (2015) Visual tracking using spatially weighted likelihood of gaussian mixtures. Comput Vis Image Underst 140:43–57

    Article  Google Scholar 

  29. Kelker D (1970) Distribution theory of spherical distributions and a location-scale parameter generalization. Sankhyā Indian J Stat Ser A 32(4):419–430

    MathSciNet  MATH  Google Scholar 

  30. Khemmar R, Gouveia M, Decoux B, Ertaud JY (2019) Real time pedestrian and object detection and tracking-based deep learning. application to drone visual tracking. In: International conference in central Europe on computer graphics, visualization and computer vision

  31. Kumar KN, Rao KS, Srinivas Y, Satyanarayana C (2015) Texture segmentation based on multivariate generalized gaussian mixture model. CMES Comput Modeling Eng Sci 107(3):201–221

    Google Scholar 

  32. Kumari GV, Rao GS, Rao BP (2021) Flower pollination-based k-means algorithm for medical image compression. Int J Adv Intell Paradig 18(2):171–192

    Google Scholar 

  33. Kunii Y, Kovacs G, Hoshi N (2017) Mobile robot navigation in natural environments using robust object tracking. In: 2017 IEEE 26th International symposium on industrial electronics (ISIE), IEEE pp. 1747–1752

  34. Lee KH, Hwang JN, Okopal G, Pitton J (2016) Ground-moving-platform-based human tracking using visual slam and constrained multiple kernels. IEEE Trans Intell Transp Syst 17(12):3602–3612

    Article  Google Scholar 

  35. Liu G, Liu S, Muhammad K, Sangaiah AK, Doctor F (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access 6:29283–29296

    Article  Google Scholar 

  36. Marvasti-Zadeh SM, Cheng L, Ghanei-Yakhdan H, Kasaei S (2021) Deep learning for visual tracking: a comprehensive survey. IEEE Trans Intell Trans Syst. https://doi.org/10.1109/TITS.2020.3046478

    Article  Google Scholar 

  37. Meghana R, Chitkara Y, Apoorva S, et al. (2019) Background-modelling techniques for foreground detection and tracking using gaussian mixture model. In: 2019 3rd International conference on computing methodologies and communication (ICCMC), IEEEpp. 1129–1134

  38. Mehboob F, Abbas M, Jiang R, Rauf A, Khan SA, Rehman S (2018) Trajectory based vehicle counting and anomalous event visualization in smart cities. Clust Comput 21(1):443–452

    Article  Google Scholar 

  39. Messaoudi A, Srairi K (2016) Colour image compression algorithm based on the dct transform using difference lookup table. Electron Lett 52(20):1685–1686

    Article  Google Scholar 

  40. Nacereddine N, Goumeidane AB, Ziou D (2019) Unsupervised weld defect classification in radiographic images using multivariate generalized gaussian mixture model with exact computation of mean and shape parameters. Comput Ind 108:132–149

    Article  Google Scholar 

  41. Najar F, Bourouis S, Bouguila N, Belghith S (2019) Unsupervised learning of finite full covariance multivariate generalized gaussian mixture models for human activity recognition. Multimed Tools Appl 78(13):18669–18691

    Article  Google Scholar 

  42. Najar F, Bourouis S, Bouguila N, Belghith S (2020) A new hybrid discriminative/generative model using the full-covariance multivariate generalized gaussian mixture models. Soft Comput 24(14):10611–10628

    Article  Google Scholar 

  43. Najar F, Bourouis S, Zaguia A, Bouguila N, Belghith S (2018) Unsupervised human action categorization using a riemannian averaged fixed-point learning of multivariate ggmm. In: International conference image analysis and recognition, Springerpp. 408–415

  44. Nam H, Baek M, Han B (2016) Modeling and propagating cnns in a tree structure for visual tracking. arXiv preprint arXiv:1608.07242

  45. Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110

    Article  Google Scholar 

  46. Onesimu JA, Kadam A, Sagayam KM, Elngar AA (2021) Internet of things based intelligent accident avoidance system for adverse weather and road conditions. J Reliab Intell Environ 7:1–15

    Article  Google Scholar 

  47. 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. J Parallel Distrib Comput 120:182–194

    Article  Google Scholar 

  48. Pascal F, Bombrun L, Tourneret JY, Berthoumieu Y (2013) Parameter estimation for multivariate generalized gaussian distributions. IEEE Trans Signal Process 61(23):5960–5971

    Article  MathSciNet  Google Scholar 

  49. Pérez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. In: European conference on computer vision, Springer pp. 661–675

  50. Rabbani M (2002) Jpeg 2000: image compression fundamentals, standards and practice. J Electron Imaging 11(2):286

    Article  Google Scholar 

  51. Rajesh G, Raajini XM, Sagayam KM, Dang H (2020) A statistical approach for high order epistasis interaction detection for prediction of diabetic macular edema. Inform Med Unlocked 20:100362

    Article  Google Scholar 

  52. 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 

  53. Sagayam KM, Ghosh A, Bhushan B et al (2021) Underwater estimation of audio signal prediction using fruit fly algorithm and hybrid wavelet neural network. J Reliab Intell Environ. https://doi.org/10.1007/s40860-021-00151-4

    Article  Google Scholar 

  54. Sagayam KM, Hemanth DJ (2018) Abc algorithm based optimization of 1-d hidden markov model for hand gesture recognition applications. Comput Ind 99:313–323

    Article  Google Scholar 

  55. Sagayam KM, Hemanth DJ, Vasanth XA, Henesy LE, Ho CC (2018) Optimization of a hmm-based hand gesture recognition system using a hybrid cuckoo search algorithm. In: Hybrid metaheuristics for image analysis pp. 87–114

  56. Sailaja V, Srinivasa Rao K, Reddy K (2010) Text independent speaker identification with finite multivariate generalized gaussian mixture model and hierarchical clustering algorithm. Int. J Comput Appl 11(11):0975–8887

    Google Scholar 

  57. Song Y, Ma C, Gong L, Zhang J, Lau RW, Yang MH (2017) Crest: Convolutional residual learning for visual tracking. In: Proceedings of the IEEE international conference on computer vision pp. 2555–2564

  58. Verdoolaege G, Scheunders P (2011) Geodesics on the manifold of multivariate generalized gaussian distributions with an application to multicomponent texture discrimination. Int J Comput Vis 95(3):265–286

    Article  Google Scholar 

  59. Voigtlaender P, Luiten J, Torr PH, Leibe B (2020) Siam r-cnn: visual tracking by re-detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp. 6578–6588

  60. Wang L, Ouyang W, Wang X, Lu H (2015) Visual tracking with fully convolutional networks. In: Proceedings of the IEEE international conference on computer vision pp. 3119–3127

  61. Wang N, Shi J, Yeung DY, Jia J (2015) Understanding and diagnosing visual tracking systems. In: Proceedings of the IEEE international conference on computer vision pp. 3101–3109

  62. Wang N, Yeung DY (2013) Learning a deep compact image representation for visual tracking. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ (eds) Advances in Neural Information Processing Systems, vol 26. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2013/file/dc6a6489640ca02b0d42dabeb8e46bb7-Paper.pdf

  63. Wu Y, Lim J, Yang MH (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848

    Article  Google Scholar 

  64. Xiong G, Feng C, Ji L (2006) Dynamical gaussian mixture model for tracking elliptical living objects. Pattern Recognit Lett 27(7):838–842

    Article  Google Scholar 

  65. Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831

    Article  Google Scholar 

  66. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey Acm computing surveys. CSUR 38(4):13

    Article  Google Scholar 

  67. Zhang T, Wiesel A, Greco MS (2013) Multivariate generalized gaussian distribution: convexity and graphical models. IEEE Trans Signal Process 61(16):4141–4148

    Article  MathSciNet  Google Scholar 

  68. Zhao Q, Yang Z, Tao H (2008) Differential earth mover‘s distance with its applications to visual tracking. IEEE Trans Pattern Anal Mach Intell 32(2):274–287

    Article  Google Scholar 

  69. Zhao X, Pu F, Wang Z, Chen H, Xu Z (2019) Detection, tracking, and geolocation of moving vehicle from uav using monocular camera. IEEE Access 7:101160–101170

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the associate editor and the anonymous reviewers for their helpful comments and suggestions.

Author information

Authors and Affiliations

  1. The Department of Electrical and Computer Engineering (ECE), Concordia University, Montreal, QC, H3G1T7, Canada

    Bingwei Ge

  2. The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, H3G1T7, Canada

    Nizar Bouguila

  3. The Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

Authors
  1. Bingwei Ge
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Nizar Bouguila
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wentao Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nizar Bouguila.

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

Ge, B., Bouguila, N. & Fan, W. Single-target visual tracking using color compression and spatially weighted generalized Gaussian mixture models. Pattern Anal Applic 25, 285–304 (2022). https://doi.org/10.1007/s10044-021-01051-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-021-01051-2

Keywords