Skip to main content
SpringerLink
Log in

Visual object tracking via coefficients constrained exclusive group LASSO

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Discriminative methods have been widely applied to construct the appearance model for visual tracking. Most existing methods incorporate online updating strategy to adapt to the appearance variations of targets. The focus of online updating for discriminative methods is to select the positive samples emerged in past frames to represent the appearances. However, the appearances of positive samples might be very dissimilar to each other; traditional online updating strategies easily overfit on some appearances and neglect the others. To address this problem, we propose an effective method to learn a discriminative template, which maintains the multiple appearances information of targets in the long-term variations. Our method is based on the obvious observation that the target appearances vary very little in a certain number of successive video frames. Therefore, we can use a few instances to represent the appearances in the scope of the successive video frames. We propose exclusive group sparse to describe the observation and provide a novel algorithm, called coefficients constrained exclusive group LASSO, to solve it in a single objective function. The experimental results on CVPR2013 benchmark datasets demonstrate that our approach achieves promising performance.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. In this paper, we assume all the feature vectors are normalized, which means \(\frac{<\mathbf {x}_1, \mathbf {x}_2>}{\Vert \mathbf {x}_1\Vert _2\Vert \mathbf {x}_2\Vert _2} = <\mathbf {x}_1, \mathbf {x}_2> = \mathbf {x}_1^T \mathbf {x}_2\).

References

  1. Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 798–805 (2006)

  2. Ahmed, J., Jafri, M.N., Shah, M., Akbar, M.: Real-time edge-enhanced dynamic correlation and predictive open-loop car-following control for robust tracking. Mach. Vis. Appl. 19(1), 1–25 (2008)

    Article  Google Scholar 

  3. Avidan, S.: Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 26(8), 1064–1072 (2004)

    Article  Google Scholar 

  4. Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)

    Article  Google Scholar 

  5. Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837 (2012)

  6. Black, M.J., Jepson, A.D.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 26(1), 63–84 (1998)

    Article  Google Scholar 

  7. Chen, L., Philip Chen, C.L., Lu, M.: A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Trans. Syst. Man Cybern. 41(5), 1263–1274 (2011)

    Article  Google Scholar 

  8. Chen, W., Zhao, Y.: Supervised kernel nonnegative matrix factorization for face recognition. Neurocomputing 205, 165–181 (2016)

    Article  Google Scholar 

  9. Chen, W.-S., Dai, X., Pan, B., Tang, Y.Y.: Semi-supervised discriminant analysis method for face recognition. Int. J. Wavelets Multiresolut. Inf. Process. 13(06), 1550049 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen, X., Yuan, X.-T., Chen, Q., Yan, S., Chua, T.-S.: Multi-label visual classification with label exclusive context. In: IEEE International Conference on Computer Vision (ICCV), pp. 834–841 (2011)

  11. Chen, X., Yuan, X., Yan, S., Tang, J., Rui, Y., Chua, T.-S.: Towards multi-semantic image annotation with graph regularized exclusive group lasso. In: International Conference on Multimedia, pp. 263–272 (2011)

  12. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 142–149 (2000)

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

  14. Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference (BMVC), pp. 65.1–65.11 (2014)

  15. Gao, J., Ling, H., Hu, W., Xing, J.: Transfer learning based visual tracking with Gaussian processes regression. In: European Conference on Computer Vision (ECCV), pp. 188–203 (2014)

  16. Ge, Q., Jing, X.-Y., Wu, F., Wei, Z., et al.: Structure-based low-rank model with graph nuclear norm regularization for noise removal. IEEE Trans. Image Process. 26, 3098–3112 (2016)

    Article  MathSciNet  Google Scholar 

  17. Gu, B., Sheng, V.S.: A robust regularization path algorithm for \(\nu \)-support vector classification. IEEE Trans. Neural Netw. Learn. Syst. 28, 1241–1248 (2017)

    Article  Google Scholar 

  18. Han, Z., Jiao, J., Zhang, B., Ye, Q., Liu, J.: Visual object tracking via sample-based adaptive sparse representation (AdaSR). Pattern Recognit. 44(9), 2170–2183 (2011)

    Article  Google Scholar 

  19. Hare, S., Golodetz, S., Saffari, A., et al.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)

    Article  Google Scholar 

  20. He, Z., Li, X., You, X., Tao, D., Tang, Y.Y.: Connected component model for multi-object tracking. IEEE Trans. Image Process. 25(8), 3698–3711 (2016)

    Article  MathSciNet  Google Scholar 

  21. He, Z., Chung, A.C.: 3-D B-spline wavelet-based local standard deviation (bwlsd): its application to edge detection and vascular segmentation in magnetic resonance angiography. Int. J. Comput. Vis. 87(3), 235–265 (2010)

    Article  Google Scholar 

  22. He, Z., Yi, S., Cheung, Y.-M., You, X., Tang, Y.Y.: Robust object tracking via key patch sparse representation. IEEE Trans. Cybern. 47, 1–11 (2016)

    Google Scholar 

  23. He, Z., You, X., Tang, Y.Y.: Writer identification of Chinese handwriting documents using hidden Markov tree model. Pattern Recognit. 41(4), 1295–1307 (2008)

    Article  MATH  Google Scholar 

  24. He, Z., You, X., Zhou, L., Cheung, Y., Jianwei, D.: Writer identification using fractal dimension of wavelet subbands in gabor domain. Integr. Comput. Aided Eng. 17(17), 157–165 (2010)

    Google Scholar 

  25. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)

    Article  Google Scholar 

  26. Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)

    Article  Google Scholar 

  27. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1822–1829 (2012)

  28. Jing, X., Wu, F.: Multi-spectral low-rank structured dictionary learning for face recognition. Pattern Recognit. 59(4), 14–25 (2016)

    Article  Google Scholar 

  29. Kong, D., Fujimaki, R., Liu, J., Nie, F., Ding, C.: Exclusive feature learning on arbitrary structures via \(\ell _{1,2}\)-norm. In: Advances in Neural Information Processing Systems, pp. 1655–1663 (2014)

  30. Lai, Z., Yong, X., Jin, Z., Zhang, D.: Human gait recognition via sparse discriminant projection learning. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1651–1662 (2014)

    Article  Google Scholar 

  31. Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems, vol. 161. SIAM, Philadelphia (1974)

    MATH  Google Scholar 

  32. Li, X., Shen, C., Dick, A., van den Hengel, A.: Learning compact binary codes for visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2419–2426 (2013)

  33. Li, X., Shen, C., Shi, Q., Dick, A., Van den Hengel, A.: Non-sparse linear representations for visual tracking with online reservoir metric learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1760–1767 (2012)

  34. Li, X., Liu, Q., He, Z., Wang, H., Zhang, C., Chen, W.-S.: A multi-view model for visual tracking via correlation filters. Knowl. Based Syst. 113, 88–99 (2016)

    Article  Google Scholar 

  35. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1313–1320 (2011)

  36. Liu, B., Yang, L., Huang, J., Meer, P., Gong, L., Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: European Conference on Computer Vision, pp. 624–637. Springer (2010)

  37. Liu, L., Chen, L.: Weighted joint sparse representation for removing mixed noise in image. IEEE Trans. Cybern. 47, 600–611 (2016)

    Article  Google Scholar 

  38. Liu, Q., Ma, X., Ou, W., Zhou, Q.: Visual object tracking with online sample selection via lasso regularization. Signal Image Video Process. 11, 881–888 (2017)

    Article  Google Scholar 

  39. Liu, R., Tang, Y.: Topological coding and its application in the refinement of sift. IEEE Trans. Cybern. 44(11), 2155–2166 (2014)

    Article  Google Scholar 

  40. Lu, H., Li, B., Zhu, J., et al.: Wound intensity correction and segmentation with convolutional neural networks. Concurr. Comput. Pract. Exp. 29, 6 (2016)

    Google Scholar 

  41. Matthews, I., Ishikawa, T., Baker, S.: The template update problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 810–815 (2004)

    Article  Google Scholar 

  42. Mei, X., Ling, H.: Robust visual tracking using \(\ell _{1}\) minimization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1436–1443 (2009)

  43. Mei, X., Ling, H., Wu, Y., Blasch, E., Bai, L.: Minimum error bounded efficient \(\ell _{1}\) tracker with occlusion detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1257–1264 (2011)

  44. Ou,W., Yuan, D., Liu, Q., Cao, Y.: Object tracking based on online representative sample selection via non-negative least square. Multimed. Tools Appl. (2017). https://doi.org/10.1007/s11042-017-4672-3

  45. Qian, J., Fang, B., Yang, W., Luan, X.: Accurate tilt sensing with linear model. IEEE Sens. J. 11(10), 2301–2309 (2011)

    Google Scholar 

  46. Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  47. Schweitzer, H., Bell, J.W., Wu, F.: Very fast template matching. In: European Conference on Computer Vision (ECCV), pp. 358–372. Springer (2002)

  48. Wang, N., Shi, J., Yeung, D.-Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3101–3109 (2015)

  49. Wang, Q., Chen, F., Xu, W., Yang, M.-H.: Online discriminative object tracking with local sparse representation. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 425–432 (2012)

  50. Weihua, O., You, X., Tao, D., Zhang, P., Tang, Y., Zhu, Z.: Robust face recognition via occlusion dictionary learning. Pattern Recognit. 47(4), 1559–1572 (2014)

    Article  Google Scholar 

  51. Weihua, O., Shujian, Y., Li, G., Jian, L., Zhang, K., Xie, G.: Multi-view non-negative matrix factorization by patch alignment framework with view consistency. Neurocomputing 204, 116–124 (2016)

    Article  Google Scholar 

  52. Wong, W.K., Lai, Z., Xu, Y., Wen, J., Ho, C.P.: Joint tensor feature analysis for visual object recognition. IEEE Trans. Cybern. 45(11), 2425–2436 (2015)

    Article  Google Scholar 

  53. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: a benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418 (2013)

  54. Yang, M., Wu, Y., Pei, M., Ma, B., Jia, Y.: Coupling semi-supervised learning and example selection for online object tracking. In: Asian Conference on Computer Vision (ACCV), pp. 476–491 (2014)

  55. Zhang, T., Ghanem, B., Liu, S., Changsheng, X., Ahuja, N.: Robust visual tracking via exclusive context modeling. IEEE Trans. Cybern. 46(1), 51–63 (2016)

    Article  Google Scholar 

  56. Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: IEEE Conference on Computer vision and pattern recognition (CVPR), pp. 1838–1845 (2012)

  57. Zhou, Q., Zheng, B., Zhu, W., Latecki, L.J.: Multi-scale context for scene labeling via flexible segmentation graph. Pattern Recognit. 59(C), 312–324 (2016)

    Article  Google Scholar 

  58. Zhou, Y., Jin, R., Hoi, S.: Exclusive lasso for multi-task feature selection. In: International Conference on Artificial Intelligence and Statistics, pp. 988–995 (2010)

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61762021, 61402122, 61672183, 61272252, 61401228, 61461008), Science and Technology Planning Project of Guanddong Province (No. 2016B090918047), Shenzhen Research Council (No.JCYJ20160406161948211, JCYJ2016022 6201453085, JSGG20150331152017052), Natural Science Foundation of Guangdong Province (No. 2015A030313544), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, Natural Science Foundation of Guizhou Province (No. 2017[1130]), the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qian jiao KY word[2015]487), the China Scholarship Council (No. 201508525007), Fund of Guizhou Educational Department (KY[2016]027), China Postdoctoral Science Foundation (Grant No. 2015M581841) and Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A).

Author information

Authors and Affiliations

  1. School of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, People’s Republic of China

    Xiao Ma & Qiao Liu

  2. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, People’s Republic of China

    Weihua Ou

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia

    Weihua Ou

  4. National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Quan Zhou

  5. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, 350121, Fuzhou, People’s Republic of China

    Quan Zhou

Authors
  1. Xiao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weihua Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weihua Ou.

Additional information

Xiao Ma and Qiao Liu contributed equally to this work and should be considered co-first authors. Weihua Ou and Quan Zhou are the corresponding authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, X., Liu, Q., Ou, W. et al. Visual object tracking via coefficients constrained exclusive group LASSO. Machine Vision and Applications 29, 749–763 (2018). https://doi.org/10.1007/s00138-018-0930-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0930-2

Keywords

Search

Navigation