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Object tracking based on online representative sample selection via non-negative least square

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Abstract

In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination of representative samples and propose a novel approach to select representative samples based on the coefficient constrained model. We formulate the objective function into a non-negative least square problem and obtain the solution utilizing standard non-negative least square. The experimental results show that the observation module of our approach outperforms several other observation modules under the same feature and motion module, such as support vector machine, logistic regression, ridge regression and structured outputs support vector machine.

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Notes

  1. BlurBody, BlurOwl, Car1, Freeman1, Freeman3, Freeman4, Human2, Human3, Human6, Human7, Human8, Human9, Lemming, Jogging-1, Jogging-2 and Suv.

  2. http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html.

References

  1. Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learning IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 983–990

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  4. Bishop C M (2006) Pattern recognition and machine learning springer

  5. Bordes A, Bottou L, Gallinari P, Weston J (2007) Solving multiclass support vector machines with larank International Conference on Machine Learning (ICML), pp 89–96

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  9. Chen Z, You X, Zhong B, Li J, Tao D Dynamically modulated mask sparse tracking, IEEE Transactions on Cybernetics, 10.1109/TCYB.2016.2577718

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

    Google Scholar 

  11. Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

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

    Google Scholar 

  13. Felzenszwalb P, Girshick R, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

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

  15. Ge Q, Jing X-Y, Wu F, Wei Z et al (2016) Structure-based low-rank model with graph nuclear norm regularization for noise removal. IEEE Trans. Image Process. doi:10.1109/TIP.2016.2639781

  16. Grabner H, Leistner C, Bischof H (2008) Semi-supervised on-line boosting for robust tracking European Conference on Computer Vision (ECCV), pp 234–247

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

    Article  Google Scholar 

  18. Henriques J, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters, Pattern Analysis and Machine Intelligence. IEEE Transactions on 37(3):583–596

    Google Scholar 

  19. He Z, Chung A C (2010) 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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  21. He Z, Yi S, Cheung Y-M, You X, Tang Y Y (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybern 47:354–364

    Google Scholar 

  22. He Z, You X, Tang Y Y (2008) Writer identification of chinese handwriting documents using hidden markov tree model. Pattern Recogn 41(4):1295–1307

    Article  MATH  Google Scholar 

  23. He Z, You X, Zhou L, Cheung Y, Du J (2010) Writer identification using fractal dimension of wavelet subbands in gabor domain. Integrated Computer Aided Engineering 17(17):157–165

    Google Scholar 

  24. Hong S, You T, Kwak S, Han B (2015) Online tracking by learning discriminative saliency map with convolutional neural network International Conference on Machine Learning (ICML), pp 597–606

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  28. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7):1409–1422

    Article  Google Scholar 

  29. Lawson C L, Hanson R J (1974) Solving least squares problems, vol 161. SIAM

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

    Article  Google Scholar 

  31. Leistner C, Saffari A, Roth PM, Bischof H (2009) On robustness of on-line boosting-a competitive study IEEE International Conference on Computer Vision (ICCV) Workshops, pp 1362–1369

    Google Scholar 

  32. Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing IEEE Conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  33. Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  36. Li Y, Zhu J, Hoi S C (2015) Reliable patch trackers: Robust visual tracking by exploiting reliable patches IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 353–361

    Google Scholar 

  37. Liu L, Chen L Weighted joint sparse representation for removing mixed noise in image

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

    Article  Google Scholar 

  39. Liu Q, Ma X, Ou W, Zhou Q Visual object tracking with online sample selection via lasso regularization, Signal, Image and Video Processing, doi:10.1007/s11760-016-1035-x

  40. Lu H, Li B, Zhu J, et al. Wound intensity correction and segmentation with convolutional neural networks, Concurrency and Computation: Practice and Experience. doi:10.1002/cpe.3927

  41. Ma X, Liu Q, He Z, Zhang X, Chen W (2016) Visual tracking via exemplar regression model. Knowl-Based Syst 106:26–37

    Article  Google Scholar 

  42. Masnadi-Shirazi H, Mahadevan V, Vasconcelos N (2010) On the design of robust classifiers for computer vision IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 779–786

    Google Scholar 

  43. Mei X, Ling H (2009) Robust visual tracking using l1 minimization IEEE International Conference on Computer Vision (ICCV), pp 1436–1443

    Google Scholar 

  44. Ou W, Yu S, Li G, Lu J, Zhang K, Xie G (2016) Multi-view non-negative matrix factorization by patch alignment framework with view consistency. Neurocomputing 204:116–124

    Article  Google Scholar 

  45. Ou W, You X, Tao D, Zhang P, Tang Y, Zhu Z (2014) Robust face recognition via occlusion dictionary learning. Pattern Recogn 47(4):1559–1572

    Article  Google Scholar 

  46. Qi Y, Zhang S, Qin L, Yao H, Huang Q, Lim J, Yang M-H (2016) Hedged deep tracking IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4303–4311

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  49. Saffari A, Leistner C, Godec M, Bischof H (2010) Robust multi-view boosting with priors European Conference on Computer Vision (ECCV), pp 776–789

    Google Scholar 

  50. Wang N, Shi J, Yeung D -Y, Jia J Understanding and diagnosing visual tracking systems, arXiv:1504.06055

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  55. Wu Y, Shen B, Ling H (2014) Visual tracking via online nonnegative matrix factorization. IEEE Trans Circuits Syst Video Technol 24(3):374–383

    Article  Google Scholar 

  56. Xu X, He L, Shimada A, Taniguchi R-I, Lu H (2016) Learning unified binary codes for cross-modal retrieval via latent semantic hashing. Neurocomputing 213:191–203

    Article  Google Scholar 

  57. Yi S, Lai Z, He Z, Cheung Y-M, Liu Y (2017) Joint sparse principal component analysis. Pattern Recogn 61:524–536

    Article  Google Scholar 

  58. Zeisl B, Leistner C, Saffari A, Bischof H (2010) On-line semi-supervised multiple-instance boosting IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1879–1879

    Google Scholar 

  59. Zhang J, Ma S, Sclaroff S (2014) Meem: Robust tracking via multiple experts using entropy minimization European Conference on Computer Vision (ECCV), pp 188–203

    Google Scholar 

  60. Zhang T, Ghanem B, Liu S, Ahuja N (2012) Low-rank sparse learning for robust visual tracking European Conference on Computer Vision (ECCV), pp 470–484

    Google Scholar 

  61. Zhang T, Liu S, Xu C, Yan S, Ghanem B, Ahuja N, Yang M-H (2015) Structural sparse tracking IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 150–158

    Google Scholar 

  62. Zhang K, Zhang L, Yang M-H (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36(10):2002–2015

    Article  Google Scholar 

  63. Zhong W, Lu H, Yang M-H (2012) Robust object tracking via sparsity-based collaborative model IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1838–1845

    Google Scholar 

  64. Zhu G, Wang J, Wu Y, Lu H (2015) Collaborative correlation tracking British Machine Vision Conference (BMVC), pp 184.1–184.12

    Google Scholar 

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Acknowledgments

We sincerely thank Xiao Ma and Quan Zhou for their good suggestions and discussion for the improvement of this paper. This work was supported by the National Nature Science Foundation of China (No. 61402122, 61461008, 61672183, 61272252), the 2014 Ph.D. Recruitment Program of Guizhou Normal University, the Outstanding Innovation Talents of Science and Technology Award Scheme of Education Department in Guizhou Province (Qianjiao KY word [2015]487), Fund of Guizhou educational department (KY[2016]027), the China Scholarship Council (No.201508525007), the Natural Science Foundation of Guangdong Province (Grant No. 2015A030313544), and the Shenzhen Research Council(Grant No. JCYJ20160406161948211, JCYJ20160226201453085).

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    Authors and Affiliations

    1. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

      Weihua Ou & Yongfeng Cao

    2. School of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

      Di Yuan & Qiao Liu

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    1. Weihua Ou
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    Correspondence to Weihua Ou.

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    Weihua Ou and Di Yuan contributed equally to this work and should be considered co-first authors

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    Ou, W., Yuan, D., Liu, Q. et al. Object tracking based on online representative sample selection via non-negative least square. Multimed Tools Appl 77, 10569–10587 (2018). https://doi.org/10.1007/s11042-017-4672-3

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