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Fast compressive tracking combined with Kalman filter

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Abstract

Compressive tracking refers to a group of high-speed algorithms for real-time object tracking. Many tracking algorithms may not generate accurate tracking results because they used fixed learning rates, and sometime lose targets when objects are occluded or deformed. To address these problems, a fast tracking algorithm combined with Kalman filter was proposed in this research. Firstly, an object location was initialized by the predicted value of Kalman filter when it was occluded, and the Kalman update was implemented only when the object was detected. The object location obtained in the Kalman update stage was used later as the initial position in the next frame. Secondly, when the distribution of positive samples satisfied a threshold, an adaptive learning rate was then updated. Finally, the naive Bayes classifier was updated with samples which had more different features. In the experiment, the proposed algorithm was compared with other state-of-the-art algorithms on seven publicly tested sequences, demonstrating that it had higher tracking accuracy and robustness in conditions such as occlusion, deformation and rotation.

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References

  1. Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632. https://doi.org/10.1109/TPAMI.2010.226

    Article  Google Scholar 

  2. Bao C, Wu Y, Ling H et al. (2012) Real time robust L1 tracker using accelerated proximal gradient approach. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1830–1837. doi:https://doi.org/10.1109/CVPR.2012.6247881

  3. Bolme SD, Beveridge RJ, Draper AB, Lui MY (2010) Visual object tracking using adaptive correlation filters. IEEE Conf Comput Vision Pattern Recognition, San Francisco, CA, USA: 2544–2550. doi:https://doi.org/10.1109/CVPR.2010.5539960

  4. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577. https://doi.org/10.1109/TPAMI.2003.1195991

    Article  Google Scholar 

  5. Danelljan M, Häger G, Khan SF, Felsberg M (2017) Discriminative scale space tracking. IEEE Trans Pattern Anal Mach Intell 39(8):1561–1575. https://doi.org/10.1109/TPAMI.2016.2609928

    Article  Google Scholar 

  6. Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1177–1184. doi:https://doi.org/10.1109/CVPR.2011.5995733

  7. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306. https://doi.org/10.1109/TIT.2006.871582

    Article  MathSciNet  MATH  Google Scholar 

  8. Han D, Lee J, Lee J et al. A low-power deep neural network online learning processor for real-time object tracking application. IEEE Transactions on Circuits and Systems I: Regular Papers Online in advance. doi:https://doi.org/10.1109/TCSI.2018.2880363

  9. He Z, Yi S, Cheung Y, You X et al (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybernet 47(2):354–364. https://doi.org/10.1109/TCYB.2016.2514714

    Google Scholar 

  10. Huang S, Hong J (2011) Moving object tracking system based on Camshift and Kalman filter. International Conference on Consumer Electronics, Communications and Networks XianNing, China, pp 1423–1426. doi:https://doi.org/10.1109/CECNET.2011.5769081

  11. Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model. IEEE Conference of Computer Vision Pattern Recognition, Providence, RI, USA: 1822–1829. doi:https://doi.org/10.1109/CVPR.2012.6247880

  12. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans Pattern Anal Mach Intell 34(7):1409–1422. https://doi.org/10.1109/TPAMI.2011.239

    Article  Google Scholar 

  13. Kim H, Park R (2018) Residual LSTM attention network for object tracking. IEEE Signal Process Letters 25(7):1029–1033. https://doi.org/10.1109/LSP.2018.2835768

    Article  Google Scholar 

  14. Liu Q, Yang J, Zhang K et al (2016) Adaptive compressive tracking via online vector boosting feature selection. IEEE Trans Cybernet PP(99):1–13. https://doi.org/10.1109/TCYB.2016.2606512

    Google Scholar 

  15. Liu Q, Hu G, Islam MM (2018) Fast visual tracking with robustifying kernelized correlation filters. IEEE Access 6:43302–43314. https://doi.org/10.1109/ACCESS.2018.2861827

    Article  Google Scholar 

  16. Mei X, Ling HB (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272. https://doi.org/10.1109/TPAMI.2011.66

    Article  Google Scholar 

  17. Nai K, Li Z, Li G et al (2018) Robust object tracking via local sparse appearance model. IEEE Trans Image Process 27(10):4958–4970. https://doi.org/10.1109/TIP.2018.2848465

    Article  MathSciNet  MATH  Google Scholar 

  18. Oron S, Bar-Hillel A, Levi D et al. (2012) Locally orderless tracking. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1940–1947. doi:https://doi.org/10.1109/CVPR.2012.6247895

  19. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  20. Ross AD, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77:125–141. https://doi.org/10.1007/s11263-007-0075-7

    Article  Google Scholar 

  21. Sevilla-Lara L, Learned-Miller E (2012) Distribution fields for tracking. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA: 1910–1917. doi:https://doi.org/10.1109/CVPR.2012.6247891

  22. Simon D (2006) Optimal state estimation: Kalman, H, infinity, and nonlinear approaches. John Wiley & Sons, New Jersey, pp 218–222

    Book  Google Scholar 

  23. Song Y, Ma C, Gong L, Zhang J, Lau HWR, Yang M (2017) CREST: convolutional residual learning for visual tracking. IEEE International Conference on Computer Vision (ICCV), Venice, Italy: 2574–2583. doi:https://doi.org/10.1109/ICCV.2017.279

  24. Wang T. and Ling H., "Gracker: a graph-based planar object tracker," IEEE Trans Pattern Anal Mach Intell, vol. 40, no. 6, pp. 1494–1501, 1 June 2018. [DOI: https://doi.org/10.1109/TPAMI.2017.2716350]

  25. Wang JT, Yang JY (2007) Object tracking based on Kalman-mean shift in occlusions. J Syst Simul 19(18):4216–4220

    Google Scholar 

  26. Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA: 2411–2418. doi:https://doi.org/10.1109/CVPR.2013.312

  27. Wu T., Lu Y. and Zhu S., "Online object tracking, learning and parsing with and-or graphs," IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 12, pp. 2465–2480, 1 Dec. 2017. doi:https://doi.org/10.1109/TPAMI.2016.2644963

  28. Yan JH, Chen SH, Ai SF et al (2014) Target tracking with improved CAMShift based on Kalman predictor. J Chin Inert Technol 22(4):536–542

    Google Scholar 

  29. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45. https://doi.org/10.1145/1177352.1177355

    Article  Google Scholar 

  30. Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recogn 46(1):397–411. https://doi.org/10.1016/j.patcog.2012.07.013

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhang T, Ghanem B, Liu S, Ahuja N (2012) Robust visual tracking via multi-task sparse learning. Proc IEEE Conference of Computer Vision Pattern Recognition, Providence, RI, USA: 2042–2049, . doi:https://doi.org/10.1109/CVPR.2012.6247908

  32. Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. European Conference on Computer Vision, Florence, Italy: 864–877. doi:https://doi.org/10.1007/978-3-642-33712-3_62

  33. Zhang K, Zhang L, Yang MH (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015. https://doi.org/10.1109/TPAMI.2014.2315808

    Article  Google Scholar 

  34. Zhao S, Zhang S, Zhang L (2018) Towards occlusion handling: object tracking with background estimation. IEEE Trans Cybernet 48(7):2086–2100. https://doi.org/10.1109/TCYB.2017.2727138

    Article  Google Scholar 

  35. Zhou X, Li J, Chen S, Cai H, Liu H (2018) Multiple perspective object tracking via context-aware correlation filter. IEEE Access 6:43262–43273. https://doi.org/10.1109/ACCESS.2018.2861824

    Article  Google Scholar 

  36. Zhu H, Porikli F (2017) Automatic refinement strategies for manual initialization of object trackers. IEEE Trans Image Process 26(2):821–835. https://doi.org/10.1109/TIP.2016.2633874

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61601358), the Natural Science Basic Research Plan in Shaanxi Province of China (2016JM6030), the Scientific Research Program funded by Shaanxi Provincial Education Department (18JK0349).

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Authors and Affiliations

  1. School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, China

    Jinguang Chen, Xiaoxing Li, Mingming Wang, Lili Ma & Bugao Xu

  2. Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76203, USA

    Bugao Xu

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  1. Jinguang Chen
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  2. Xiaoxing Li
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Correspondence to Bugao Xu.

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Chen, J., Li, X., Wang, M. et al. Fast compressive tracking combined with Kalman filter. Multimed Tools Appl 78, 22463–22477 (2019). https://doi.org/10.1007/s11042-019-7514-7

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