Skip to main content
SpringerLink
Log in

Object Tracking Algorithm based on Improved Context Model in Combination with Detection Mechanism for Suspected Objects

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Built upon the methodology of “spatio-temporal context”, a simple yet robust object tracking method is proposed for solving the occlusion problems in this paper. This algorithm makes full use of the context information of the object and its local background to calculate the features, which maximumlly improve the occlusion predictive response and recapture accuracy. Firstly, an early warning mechanism is adopted to realize the occlusion detection. Once the object is fully occluded, the object position with accurate motion information saved in the early warning is predicted and memory tracking model is used to delete the suspected object region, which reduces the matching complexity. Finally, a confidence strategy for similarity measurement is adopted to capture the suspected object when the object appears, and the optimal confidence is introduced to get an adaptive update model. Many simulation experiments in benchmark videos show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms.

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

Similar content being viewed by others

References

  1. Bay H, Tuytelaars T, Van Gool L, et al. (2006) SURF: speeded up robust features[C]. European Conference on Computer Vision, 404–417

  2. Chen X, Davis J (2008) An occlusion metric for selecting robust camera configurations. machine vision applications 19. 4 217–222.

  3. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern B 43(4):996–1002

    Article  Google Scholar 

  4. Henriques J, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV. pp. 702–715 16

  5. Henriques JF, Caseiro R, Martins P et al (2015) High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Trans Pattern Anal Mach Intell 37(3):583–596

    Article  Google Scholar 

  6. Jiang H, Li J, Wang D et al. (2016) Multi-feature tracking via adaptive weights[J]. Neurocomputing, 189–201

  7. Jun K, Min J, Xiao-Wei T, Yi-Ning S, Ke J, Guang-Rui W (2015) Target tracking by compressive sensing based on Gaussian differential graph[J]. J Infrared Millimeter Waves 34(1):100–105

    Google Scholar 

  8. Kalal, M, Matas, (2011) Tracking Learning Detection, Pattern Analysis and Machine Intelligence

  9. Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: Bootstrapping binary classifiers by structural constraints. In: CVPR. pp. 49–56

  10. Liu, Ye et al. Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. International Conference on Pattern Recognition IEEE, 2012:898–901

  11. Liu Y et al. (2015) Action2Activity: recognizing complex activities from sensor data. International Conference on Artificial Intelligence AAAI Press, 1617–1623

  12. Liu Y et al (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  13. Liu L, Cheng L, Liu Y et al. (2016) Recognizing Complex Activities by a Probabilistic Interval-Based Model[C]// Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, 1266–1272

  14. Lowe DG (2004) Distinctive Image Features from Scale-Invariant Keypoints[J]. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  15. Ross DA, Lim J, Lin R, et al. (2008) Incremental Learning for Robust Visual Tracking [J]. International Journal of Computer Vision, 125–141

  16. Ross D, Lim J, Lin R, Yang MH (2008) Incremental learning for robust visual tracking. IJCV 77(1):125–141

    Article  Google Scholar 

  17. Su Z, Yuming Z, Liang H (2017) A multi-feature compressed target tracking algorithm based on redundant dictionary [J]. Acta Tribune of China 38(06):1140–1146

    Google Scholar 

  18. Tao XIE, Ensi WU (2017) A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning. JEIT 40(3):602–609

    Google Scholar 

  19. Torr HS (2011) Struck: Structured output tracking with kernels. In: ICCV. pp. 263–270

  20. Valmadre J et al. (2017) End-to-End Representation Learning for Correlation Filter Based Tracking. computer vision and pattern recognition 5000–5008

  21. Wen L, Cai Z, Lei Z, Yi D, Li S (2012) Online spatio-temporal structure context learning for visual tracking. In: ECCV. pp. 716–729

  22. Wu Y et al. (2017) Nondestructive measurement of internal quality attributes of apple fruit by using NIR spectroscopy. Multimed Tools Appl 1–17

  23. Yang C, Yu C, Yan M, Yuan X et al (2016) A Particle Filter Infrared Target Tracking Algorithm Based on Feature Fusion[J]. Inf Technol 38(03):211–217

    Google Scholar 

  24. Zhang KH, Song HH (2013) Real-time visual tracking Via online weighted multiple instance learning[J]. Pattern Recogn 46(1):397–411

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhang KH, Zhang L, Yang MH et al (2013) Robust object tracking via active feature selection[J]. IEEE Trans Circuits syst Video Technol 23(11):1957–1967

    Article  Google Scholar 

  26. Zhang KH, Zhang L, Yang MH (2013) Real-time object tracking via online discriminative feature selection[J]. IEEE Trans Image Process 22(12):4664–4677

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhang KH, Zhang L, Yank MH (2014) Fast compressive tracking[J]. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015

    Article  Google Scholar 

  28. Zhang KH, Zhang L, Liu Q S, et al. (2014) Fast visual tracking via dense spatio-temporal context learning[C]//European Conference on Computer Vision, 127–141

  29. Zhang K, Zhang L, Yang M et al (2014) Fast Compressive Tracking.[J]. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of Shanxi Province (No.2014021022-5), and the Technological Project of State Grid Corporation of China (No.5205301500). The authors thank Zhang Kaihua and Kalal for providing their results.

Author information

Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China

    Xiuyan Tian, Haifang Li & Hongxia Deng

  2. Department of Economy & Management, Yuncheng University, Yuncheng, 044000, China

    Xiuyan Tian

Authors
  1. Xiuyan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haifang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongxia Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuyan Tian.

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

Tian, X., Li, H. & Deng, H. Object Tracking Algorithm based on Improved Context Model in Combination with Detection Mechanism for Suspected Objects . Multimed Tools Appl 78, 16907–16922 (2019). https://doi.org/10.1007/s11042-018-7025-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7025-y

Keywords

Search

Navigation