Skip to main content

Advertisement

Springer Nature Link
Log in

A robust object tracking framework based on a reliable point assignment algorithm

  • Published:
Frontiers of Information Technology & Electronic Engineering Aims and scope Submit manuscript

Abstract

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

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

Similar content being viewed by others

References

  • Bay, H., Ess, A., Tuytelaars, T., et al., 2008. Speeded-up robust features (SURF). Comput. Vis. Image Understand., 110(3): 346–359. http://dx.doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  • Brox, T., Bruhn, A., Papenberg, N., et al., 2004. High accuracy optical flow estimation based on a theory for warping. European Conf. on Computer Vision, p.25–36. http://dx.doi.org/10.1007/978-3-540-24673-2_3

    MATH  Google Scholar 

  • Cheng, C.W., Ou, W.L., Fan, C.P., 2016. Fast ellipse fitting based pupil tracking design for human-computer interaction applications. IEEE Int. Conf. on Consumer Electronics, p.445–446. http://dx.doi.org/10.1109/ICCE.2016.7430685

    Google Scholar 

  • Dalal, N., Triggs, B., 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.886–893. http://dx.doi.org/10.1109/CVPR.2005.177

    Google Scholar 

  • Elhamod, M., Levine, M.D., 2013. Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans. Intell. Transp. Syst., 14(2): 688–699. http://dx.doi.org/10.1109/TITS.2012.2228640

    Article  Google Scholar 

  • Elmenreich, W., Koplin, M.A., 2011. Time-triggered object tracking subsystem for advanced driver assistance systems. Elektrotechn. Inform., 128(6): 203–208. http://dx.doi.org/10.1007/s00502-011-0004-x

    Article  Google Scholar 

  • Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing (2nd Ed.). Prentice Hall, Inc., New Jersey.

    Google Scholar 

  • Harris, C., Stephens, M., 1988. A combined corner and edge detector. Proc. Alvey Vision Conf., p.147–151. http://dx.doi.org/10.5244/C.2.23

    Google Scholar 

  • Henriques, J.F., Caseiro, R., Martins, P., et al., 2015. High-speed tracking with kernelized correlation filters. IEEE Trans. Patt. Anal. Mach. Intell., 37(3): 583–596. http://dx.doi.org/10.1109/TPAMI.2014.2345390

    Article  Google Scholar 

  • Jeong, J.M., Yoon, T.S., Park, J.B., 2014. Kalman filter based multiple objects detection-tracking algorithm robust to occlusion. Proc. SICE Annual Conf., p.941–946. http://dx.doi.org/10.1109/SICE.2014.6935235

    Google Scholar 

  • Jia, C.X., Wang, Z.L., Wu, X., et al., 2015. A trackinglearning-detection (TLD) method with local binary pattern improved. IEEE Int. Conf. on Robotics and Biomimetics, p.1625–1630. http://dx.doi.org/10.1109/ROBIO.2015.7419004

    Google Scholar 

  • Jung, Y., Yoon, Y., 2015. Behavior tracking model in dynamic situation using the risk ratio EM. Int. Conf. on Information Networking, p.444–448. http://dx.doi.org/10.1109/ICOIN.2015.7057942

    Google Scholar 

  • Kalal, Z., Mikolajczyk, K., Matas, J., 2010a. Forwardbackward error: automatic detection of tracking failures. 20th Int. Conf. on Pattern Recognition, p.23–26. http://dx.doi.org/10.1109/ICPR.2010.675

    Google Scholar 

  • Kalal, Z., Matas, J., Mikolajczyk, K., 2010b. P-N learning: bootstrapping binary classifiers by structural constraints. IEEE Conf. on Computer Vision and Pattern Recognition, 49–56. http://dx.doi.org/10.1109/CVPR.2010.5540231

    Google Scholar 

  • Kalal, Z., Mikolajczyk, K., Matas, J., 2012. Trackinglearning-detection. IEEE Trans. Patt. Anal. Mach. Intell., 34(7): 1409–1422. http://dx.doi.org/10.1109/TPAMI.2011.239

    Article  Google Scholar 

  • Kalman, R.E., 1960. A new approach to linear filtering and prediction problems. J. Basic Eng., 82(1): 35–45. http://dx.doi.org/10.1115/1.3662552

    Article  Google Scholar 

  • Kaur, H., Sahambi, J.S., 2015. Vehicle tracking using fractional order Kalman filter for non-linear system. Int. Conf. on Computing, Communication and Automation, p.474–479. http://dx.doi.org/10.1109/CCAA.2015.7148423

    Chapter  Google Scholar 

  • Kong, H., Akakin, H.C., Sarma, S.E., 2013. A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans. Cybern., 43(6): 1719–1733. http://dx.doi.org/10.1109/TSMCB.2012.2228639

    Article  Google Scholar 

  • Li, Y., Zhu, J.K., Hoi, S.C.H., 2015. Reliable patch trackers: robust visual tracking by exploiting reliable patches. IEEE Conf. on Computer Vision and Pattern Recognition, p.353–361. http://dx.doi.org/10.1109/CVPR.2015.7298632

    Google Scholar 

  • Liu, S., Zhang, T.Z., Cao, X.C., et al., 2016. Structural correlation filter for robust visual tracking. IEEE Conf. on Computer Vision and Pattern Recognition, p.4312–4320. http://dx.doi.org/10.1109/CVPR.2016.467

    Google Scholar 

  • Liu, T., Wang, G., Yang, Q.X., 2015. Real-time part-based visual tracking via adaptive correlation filters. IEEE Conf. on Computer Vision and Pattern Recognition, p.4902–4912. http://dx.doi.org/10.1109/CVPR.2015.7299124

    Google Scholar 

  • Lowe, D.G., 2004. Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vis., 60(2): 91–110. http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  • Ning, G.H., Zhang, Z., Huang, C., et al., 2016. Spatially supervised recurrent convolutional neural networks for visual object tracking. arXiv:1607.05781v1.

  • Prakash, U.M., Thamaraiselvi, V.G., 2014. Detecting and tracking of multiple moving objects for intelligent video surveillance systems. 2nd Int. Conf. on Current Trends in Engineering and Technology, p.253–257. http://dx.doi.org/10.1109/ICCTET.2014.6966297

    Google Scholar 

  • Redmon, J., Divvala, S., Girshick, R., et al., 2016. You only look once: unified, real-time object detection. IEEE Conf. on Computer Vision and Pattern Recognition, p.779–788. http://dx.doi.org/10.1109/CVPR.2016.91

    Google Scholar 

  • Sun, X., Yao, H.X., Zhang, S.P., 2010. A refined particle filter method for contour tracking. SPIE, 7744:77441M. http://dx.doi.org/10.1117/12.863450

    Google Scholar 

  • Tarkov, M.S., Dubynin, S.V., 2013. Real-time object tracking by CUDA-accelerated neural network. J. Comput. Sci. Appl., 1(1): 1–4. http://dx.doi.org/10.12691/jcsa-1-1-1

    Google Scholar 

  • Viola, P., Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.511–518. http://dx.doi.org/10.1109/CVPR.2001.990517

    Google Scholar 

  • Xu, F., Gao, M., 2010. Human detection and tracking based on HOG and particle filter. 3rd Int. Congress on Image and Signal Processing, p.1503–1507. http://dx.doi.org/10.1109/CISP.2010.5646273

    Google Scholar 

  • Yu, H.M., Zeng, X., 2015. Visual tracking combined with ranking vector SVM. J. Zhejiang Univ. (Eng. Sci.), 49(6): 1015–1021 (in Chinese). http://dx.doi.org/10.3785/j.issn.1008-973X.2015.06.003

    Google Scholar 

  • Yu, W.S., Tian, X.H., Hou, Z.Q., et al., 2015. Multi-scale mean shift tracking. IET Comput. Vis., 9(1): 110–123. http://dx.doi.org/10.1049/iet-cvi.2014.0077

    Article  Google Scholar 

  • Zhang, R.F., Xiao, H.H., Deng, T., et al., 2016. A robust point detection algorithm based on wavelet transform for visual tracking. Int. Congress on Image and Signal Processing, Biomedical Engineering and Informatics, p.1–5. http://dx.doi.org/10.1109/CISP-BMEI.2016.7852672

    Google Scholar 

Download references

Acknowledgments

The authors thank Professor Sheng-ming JIANG for his good advice.

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Rong-feng Zhang, Gui-hong Wang, Jing-lun Shi & Quan-sheng Guan

  2. School of Electronic and Information Engineering, Guangzhou College of South China University of Technology, Guangzhou, 510800, China

    Rong-feng Zhang

  3. Information Network Engineering and Research Center, South China University of Technology, Guangzhou, 510641, China

    Ting Deng

Authors
  1. Rong-feng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ting Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gui-hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing-lun Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quan-sheng Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ting Deng.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61671213 and 61302058) and the Guangzhou Key Lab of Body Data Science (No. 201605030011)

A preliminary version was presented at the 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Oct. 15–17, 2016, Datong, China

ORCID: Ting DENG, http://orcid.org/0000-0001-9394-5430

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Rf., Deng, T., Wang, Gh. et al. A robust object tracking framework based on a reliable point assignment algorithm. Frontiers Inf Technol Electronic Eng 18, 545–558 (2017). https://doi.org/10.1631/FITEE.1601464

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/FITEE.1601464

Key words

CLC number