Skip to main content
SpringerLink
Log in

Target tracking algorithm based on a broad learning system

  • Moop
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

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

References

  1. Chen C L P, Liu Z L. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst, 2018, 29: 10–24

    Article  MathSciNet  Google Scholar 

  2. Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF). Comput Vision Image Underst, 2008, 110: 346–359

    Article  Google Scholar 

  3. Zhang K H, Yang M H. Real-time compressive tracking. In: Proceedings of the 12th European Conference on Computer Vision, Florence, 2012. 864–877

  4. Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 583–596

    Article  Google Scholar 

  5. Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell, 2012, 34: 1409–1422

    Article  Google Scholar 

  6. Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86: 2278–2324

    Article  Google Scholar 

  7. Danelljan M, Robinson A, Khan F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Proceedings of the 14th European Conference on Computer Vision, Amsterdam, 2016. 472–488

  8. Hyeonseob N, Bohyung H. Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 4293–4302

  9. Wu Y, Lim J, Yang M H. Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell, 2015, 37: 1834–1848

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 51939001, 61976033, U1813203, 61803064, 61751202), Natural Foundation Guidance Plan Project of Liaoning (Grant No. 2019-ZD-0151), Science & Technology Innovation Funds of Dalian (Grant No. 2018J11CY022), and Fundamental Research Funds for the Central Universities (Grant No. 3132019345).

Author information

Authors and Affiliations

  1. Navigation College, Dalian Maritime University, Dalian, 116026, China

    Dan Zhang, Tieshan Li, C. L. Philip Chen & He Yang

  2. Innovation and Entrepreneurship Education College, Dalian Minzu University, Dalian, 116600, China

    Dan Zhang

  3. Computer Science and Engineering College, South China University of Technology, Guangzhou, 510641, China

    C. L. Philip Chen

  4. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Tieshan Li

Authors
  1. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tieshan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. L. Philip Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. He Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tieshan Li.

Additional information

Conclusions and discussion

Based on our experimental results, the following two aspects will be discussed. (1) Tracking adaptability: First, we trained an accurate information evaluator. BLS is the process of acquiring sparse features. Sparse feature learning models are attractive for exploring the essential characteristics of tracking data. Based on statistical target occlusion and loss, we can adjust the candidate region search and SURF feature matching. By using such a tracking strategy, we can effectively enhance tracking adaptability. (2) Time consumption: Our method has a small time overhead because BLS has few parameters and is solved using ridge regression. Experimental results demonstrated the effectiveness of the proposed algorithm, but it still has some deficiencies. Taking tracking speed as an example, there is still room for further improvement. Robustness to long-term occlusion and loss, as well as scale variation, must be improved. Additionally, online tracking must be implemented. Follow-up research will focus on these issues and additional studies will be required to develop faster and more robust online target tracking systems.

Electronic Supplementary Material

Target Tracking Algorithm Based on a Broad Learning System

Supplementary material, approximately 31 MB.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, D., Li, T., Chen, C.L.P. et al. Target tracking algorithm based on a broad learning system. Sci. China Inf. Sci. 65, 154201 (2022). https://doi.org/10.1007/s11432-020-3272-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3272-y

Search

Navigation