Advertisement

Object tracking via dense SIFT features and low-rank representation

  • Yong Wang
  • Xinbin Luo
  • Lu Ding
  • Jingjing Wu
Methodologies and Application
  • 40 Downloads

Abstract

In this paper, we present a low-rank sparse tracking method which builds upon the particle filtering framework. The proposed method learns the local dense scale-invariant feature transform features corresponding to candidate samples jointly by exploiting the underlying sparse and low-rank constraints. Furthermore, the alternating direction method of multipliers method guarantees the optimization equation can be solved accurately and robustly. We evaluate our proposed tracking method against 9 state-of-the-art trackers on a set of 64 challenging sequences. Experimental results show that the proposed method performs favorably against state-of-the-art trackers in terms of accuracy.

Keywords

Visual object tracking Dense SIFT features Sparse and low-rank constraints Alternating direction method of multipliers 

Notes

Acknowledgements

This paper is jointly supported by the National Natural Science Foundation of China (61305016) and Fundamental Research Funds for the Central Universities (Grant No. JUSRP1059).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of this paper.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 798–805Google Scholar
  2. Avidan S (2005) Ensemble tracking. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 494–501Google Scholar
  3. Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  4. Bao C, Wu Y, Ling H, Ji H (2012) Real time robust L1 tracker using accelerated proximal gradient approach. In: IEEE conference on computer vision and pattern recognition (CVPR), Rhode IslandGoogle Scholar
  5. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122CrossRefGoogle Scholar
  6. Comaniciu D, Member VR, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575CrossRefGoogle Scholar
  7. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The PASCAL Visual Object Classes Challenge 2010 (VOC2010) ResultsGoogle Scholar
  8. Grabner M, Grabner H, Bischof H (2007) Learning features for tracking. In: IEEE conference on computer vision and pattern recognition, CVPR’07. IEEE, pp 1–8Google Scholar
  9. Hare S, Saffari A, Torr PHS (2011) Struck: structured output tracking with kernels. In: Proceedings of IEEE ICCV, November, pp 263–270Google Scholar
  10. Hare S, Saffari A, Torr PHS (2012) Efficient online structured output learning for keypoint-based object tracking. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1894–1901Google Scholar
  11. Ho HT, Chellappa R (2014) Automatic head pose estimation using randomly projected dense SIFT descriptors, vol 8556, pp 153–156Google Scholar
  12. Hong Z, Mei X, Prokhorov D, Tao D (2013) Tracking via robust multi-task multi-view joint sparse representation. In: ICCVGoogle Scholar
  13. Isard M, Blake A (1998) CONDENSATION—conditional density propagation for visual tracking. IJCV 29(1):5–28CrossRefGoogle Scholar
  14. Jia X, Lu H, Yang M-H (2012) Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1822–1829Google Scholar
  15. Kalal Z, Matas J, Mikolajczyk K (2010) P-N learning: bootstrapping binary classifiers by structural constraints. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 49–56Google Scholar
  16. Kwon J, Lee KM (2010) Visual tracking decomposition. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1269–1276Google Scholar
  17. Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel AVD (2013) A survey of appearance models in visual object tracking. ACM Trans Intell Syst Technol 4(4):58CrossRefGoogle Scholar
  18. Liu C, Yuen J, Torralba A, Sivic J, Freeman W (2008) SIFT flow: dense correspondence across different scenes. In: Proceedings of ECCV, pp 28–42Google Scholar
  19. Lowe DJ (2004) Distinctive image features from scale-invariant keypoints. IJCV 60(2):91–110MathSciNetCrossRefGoogle Scholar
  20. Ma B, Shen J, Liu Y, Hu H, Shao L, Li X (2015) Visual tracking using strong classifier and structural local sparse descriptors. IEEE Trans Multimed 17(10):1818–1828CrossRefGoogle Scholar
  21. Ma B, Huang L, Shen J, Shao L, Yang M-H, Porikli F (2016) Visual tracking under motion blur. IEEE Trans Image Process 25(12):5867–5876MathSciNetCrossRefGoogle Scholar
  22. Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272CrossRefGoogle Scholar
  23. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefGoogle Scholar
  24. Quattoni A, Carreras X, Collins M, Darrell T (2009) An efficient projection for L1, infinity regularization. In: International conference on machine learning, pp 857–864Google Scholar
  25. Ren X, Malik J (2007) Tracking as repeated figure/ground segmentation. In: Proceedings of IEEE conference on computer vision and pattern recognition, June, pp 1–8Google Scholar
  26. Ross D, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1):125–141CrossRefGoogle Scholar
  27. Wang J, Li J, Yau W, Sung E (2010) Boosting dense SIFT descriptors and shape contexts of face images for gender recognition. In: Proceedings of CVPR, pp 96–102Google Scholar
  28. Wang S, Lu H, Yang F, Yang M-H (2011) Superpixel tracking. In: Proceedings of IEEE international conference on computer vision, November, pp 1323–1330Google Scholar
  29. Wang D, Lu H, Yang M (2013a) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325MathSciNetCrossRefGoogle Scholar
  30. Wang D, Lu H, Yang M-H (2013b) Least soft-thresold squares tracking. In: CVPR, pp 2371–2378Google Scholar
  31. Wang Y, Hu S, Wu S (2015) Visual tracking based on group sparsity learning. Mach Vis Appl 26(1):127–139CrossRefGoogle Scholar
  32. Wright J, Ganesh A, Rao S, Peng Y, Ma Y (2009) Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization, In Advances in neural information processing systems, pp 2080–2088Google Scholar
  33. Xiao Z, Lu H, Wang D (2014) L2-RLS-based object tracking. IEEE Trans Circuits Syst Video Technol 24(8):1301–1309CrossRefGoogle Scholar
  34. Yang F, Lu H, Yang M (2014) Robust superpixel tracking. IEEE Trans Image Process 23(4):1639–1651MathSciNetCrossRefGoogle Scholar
  35. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):13–32CrossRefGoogle Scholar
  36. Yuan X, Yan S (2010) Visual classification with multi-task joint sparse representation. In: IEEE conference on computer vision and pattern recognition, pp 3493–3500Google Scholar
  37. Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46(1):397–411MathSciNetCrossRefGoogle Scholar
  38. Zhang T, Ghanem B, Liu S, Ahuja N (2012a) Robust visual tracking via multi-task sparse learning. In: IEEE conference on computer vision and pattern recognition, pp 1–8Google Scholar
  39. Zhang K, Zhang L, Yang M-H (2012b) Real-time compressive tracking. In: Proceedings of European conference on computer vision, vol 3, pp 864–877, Florence, Italy, OctoberCrossRefGoogle Scholar
  40. Zhao L, Li X, Xiao J, Wu F, Zhuang Y (2015) Metric learning driven multi-task structured output optimization for robust keypoint tracking. In: Twenty-ninth AAAI conference on artificial intelligenceGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina
  4. 4.School of Mechanical EngineeringJiangnan UniversityWuxiChina

Personalised recommendations