Skip to main content
SpringerLink
Log in

A Robust Parallel Object Tracking Method for Illumination Variations

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Illumination variation often occurs in visual tracking, which has a severe impact on the system performance. Many trackers based on Discriminative correlation filter (DCF) have recently obtained promising performance, showing robustness to illumination variation. However, when the target objects undergo significant appearance variation due to intense illumination variation, the features extracted from the object will not have the ability to be discriminated from the background, which causes the tracking algorithm to lose the target in the scene. In this paper, in order to improve the accuracy and robustness of the Discriminative correlation filter (DCF) trackers under intense illumination variation, we propose a very effective strategy by performing multiple region detection and using alternate templates (MRAT). Based on parallel computation, we are able to perform simultaneous detection of multiple regions, equivalently enlarging the search region. Meanwhile the alternate template is saved by a template update mechanism in order to improve the accuracy of the tracker under strong illumination variation. Experimental results on large-scale public benchmark datasets show the effectiveness of the proposed method compared to state-of-the-art 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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Yilmaz A, Javed O, Shah M (2006) Object tracking: A survey. ACM Computing Surveys (CSUR) 38(4):13

    Article  Google Scholar 

  2. Wang N, Shi J, Yeung D Y, et al (2015) Understanding and diagnosing visual tracking systems. Proceedings of the IEEE International Conference on Computer Vision 3101–3109

  3. Yang H, Shao L, Zheng F et al (2011) Recent advances and trends in visual tracking: A review. Neurocomputing 74(18):3823–3831

    Article  Google Scholar 

  4. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-Learning-Detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 34(7):1409–1422

    Article  Google Scholar 

  5. Hare S, Saffari A, Torr PHS (2012) Struck: Structured output tracking with kernels. IEEE International Conference on Computer Vision. IEEE, 263–270

  6. Zhang J, Ma S, Sclaroff S (2014) MEEM: robust tracking via multiple experts using entropy minimization, European Conference on Computer Vision. Springer, Cham, pp 188–203

    Google Scholar 

  7. Danelljan M, Shahbaz Khan F, Felsberg M, et al (2014) Adaptive color attributes for real-time visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1090–1097

  8. Montero A S, Lang J, Laganiere R (2015) Scalable kernel correlation filter with sparse feature integration. Computer Vision Workshop (ICCVW), 2015 IEEE International Conference on. IEEE, 587–594

  9. Zhang K, Zhang L, Liu Q et al (2014) Fast visual tracking via dense spatio-temporal context learning. European Conference on Computer Vision. Springer, Cham, pp 127–141

    Google Scholar 

  10. Bibi A, Mueller M, Ghanem B (2016) Target response adaptation for correlation filter tracking. European Conference on Computer Vision. Springer International Publishing, 419–433

  11. Danelljan M, Hager G, Shahbaz Khan F, et al (2016) Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1430–1438

  12. Kumar BVKV, Mahalanobis A, Juday RD (2005) Correlation pattern recognition. Cambridge University Press, Cambridge

    Book  Google Scholar 

  13. Bolme D S, Beveridge J R, Draper B A, et al. (2010) Visual object tracking using adaptive correlation filters. Computer Vision and Pattern Recognition. IEEE, 2544–2550

  14. Danelljan M, Robinson A, Khan FS et al (2016) Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking, European Conference on Computer Vision. Springer, Cham, pp 472–488

    Google Scholar 

  15. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 1: 886–893

  16. Chen Z, Hong Z, Tao D (2015) An experimental survey on correlation filter-based tracking. arXiv preprint arXiv:1509.05520

  17. Henriques JF, Caseiro R, Martins P et al (2012) Exploiting the circulant structure of tracking-by-detection with kernels, European conference on computer vision. Springer, Berlin, pp 702–715

    Google Scholar 

  18. Rifkin R, Yeo G, Poggio T (2003) Regularized least-squares classification. NATO Science Series Sub Series III Computer and Systems Sciences 190:131–154

    Google Scholar 

  19. Henriques JF, Caseiro R, Martins P et al (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  20. Van De Weijer J, Schmid C, Verbeek J et al (2009) Learning color names for real-world applications. IEEE Trans Image Process 18(7):1512–1523

    Article  MathSciNet  Google Scholar 

  21. Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417

    Article  Google Scholar 

  22. Ma C, Yang X, Zhang C, et al (2015) Long-term correlation tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5388–5396

  23. Ma C, Huang J B, Yang X, et al (2015) Hierarchical convolutional features for visual tracking. Proceedings of the IEEE International Conference on Computer Vision. 3074–3082

  24. Qi Y, Zhang S, Qin L, et al (2016) Hedged deep tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4303–4311

  25. Sun X, Rosin PL, Martin RR et al (2009) Bas-relief generation using adaptive histogram equalization. IEEE Trans Vis Comput Graph 15(4):642–653

    Article  Google Scholar 

  26. Li L, Huang W, Gu IYH et al (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13(11):1459–1472

    Article  Google Scholar 

  27. Chen L H, Yang Y H, Chen C S, et al (2011) Illumination invariant feature extraction based on natural images statistics—Taking face images as an example. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 681–688

  28. Silveira G, Malis E (2007) Real-time visual tracking under arbitrary illumination changes. Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 1–6

  29. Zhu G, Zhang S, Chen X et al (2007) Efficient illumination insensitive object tracking by normalized gradient matching. IEEE Signal Processing Letters 14(12):944–947

    Article  Google Scholar 

  30. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, Cambridge

    Google Scholar 

  31. Li X, Hu W, Shen C et al (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58

    Article  Google Scholar 

  32. Wu Y, Lim J, Yang M-H (2013) Online object tracking: A benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2411–2418

  33. Zhu G, Wang J, Wu Y, et al (2015) Collaborative Correlation Tracking. BMVC, 184.1–184.12

  34. Choi J, Chang HJ, Yun S, et al (2017) Attentional Correlation Filter Network for Adaptive Visual Tracking. IEEE Conference on Computer Vision and Pattern Recognition. IEEE

  35. Bertinetto L, Valmadre J, Golodetz S, et al (2016) Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1401–1409

  36. Cai B, Xu X, Xing X et al (2016) BIT: Biologically inspired tracker. IEEE Trans Image Process 25(3):1327–1339

    Article  MathSciNet  Google Scholar 

  37. Danelljan M, Häger G, Khan FS et al (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575

    Article  Google Scholar 

  38. Bertinetto L, Valmadre J, Henriques J F, et al (2016) Fully-convolutional Siamese networks for object tracking. European Conference on Computer Vision. Springer International Publishing, 850–865

  39. Li Y, Zhu J, Hoi SCH (2015) Reliable patch trackers: Robust visual tracking by exploiting reliable patches. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 353–361

  40. Valmadre J, Bertinetto L, Henriques J F, et al (2017) End-to-end representation learning for Correlation Filter based tracking. arXiv preprint arXiv:1704.06036

  41. Kumar V (2003) Introduction to parallel computing: design and analysis of algorithms. 22(2):N12

  42. Dongarra J, Foster I, Fox G et al (2003) Sourcebook of parallel computing. Morgan Kaufmann Publishers Inc, Burlington

    Google Scholar 

  43. Yu Y, Gunda P K, Isard M (2009) Distributed aggregation for data-parallel computing: interfaces and implementations. ACM SIGOPS, Symposium on Operating Systems Principles. ACM, 247–260

  44. Culler DE, Singh JP, Gupta A (1999) Parallel computer architecture: a hardware/software approach. Gulf Professional Publishing, Houston

    Google Scholar 

  45. Flynn MJ (1972) Some computer organizations and their effectiveness. IEEE Trans Comput 100(9):948–960

    Article  Google Scholar 

  46. Grama A (2003) Introduction to parallel computing. Pearson Education

  47. Kumar VP, Gupta A (1994) Analyzing scalability of parallel algorithms and architectures. Journal of Parallel and Distributed Computing 22(3):379–391

    Article  Google Scholar 

  48. Kwon J, Lee KM (2010) Visual tracking decomposition. Computer Vision and Pattern Recognition. IEEE, 1269–1276

  49. Lan X, Ma A J, Yuen PC (2014) Multi-cue visual tracking using robust feature-level fusion based on joint sparse representation. Computer Vision and Pattern Recognition. IEEE, 1194–1201

  50. Lan X, Ma AJ, Yuen PC et al (2015) Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 24(12):5826

    Article  MathSciNet  Google Scholar 

  51. Lan X, Zhang S, Yuen PC (2016) Robust joint discriminative feature learning for visual tracking, International Joint Conference on Artificial Intelligence. AAAI Press, Quebec, pp 3403–3410

    Google Scholar 

  52. Lan X, Yuen P C, Chellappa R. Robust MIL-Based Feature Template Learning for Object Tracking. AAAI. 2017: 4118–4125

  53. Lan X, Zhang S, Yuen PC, et al (2017) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Transactions on Image Processing

  54. Zhou T, Zhu M, Zeng D et al (2017) Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion. Math Probl Eng 2017:1–8

    Google Scholar 

  55. Ding G, Chen W, Zhao S et al (2017) Real-Time Scalable Visual Tracking via Quadrangle Kernelized Correlation Filters. IEEE Trans Intell Transp Syst 19(1):140–150

    Article  Google Scholar 

  56. Zhang B, Luan S, Chen C et al (2017) Latent Constrained Correlation Filter. IEEE Trans Image Process PP(99):1

    MATH  Google Scholar 

  57. Han J, Pauwels EJ, Zeeuw PMD et al (2012) Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment. IEEE Trans Consum Electron 58(2):255–263

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (No:61502254), Natural Science Foundation of Inner Mongolia [No. 2014BS0602].

Author information

Authors and Affiliations

  1. College of Computer Science, Inner Mongolia University, Hohhot, 010012, China

    Shuai Liu, Gaocheng Liu & Huiyu Zhou

  2. Inner Mongolia Key Laboratory of Social Comuting and Data Processing, Hohhot, 010012, China

    Shuai Liu, Gaocheng Liu & Huiyu Zhou

Authors
  1. Shuai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaocheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huiyu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huiyu Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Liu, G. & Zhou, H. A Robust Parallel Object Tracking Method for Illumination Variations. Mobile Netw Appl 24, 5–17 (2019). https://doi.org/10.1007/s11036-018-1134-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1134-8

Keywords

Search

Navigation