KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권10호
  • /
  • Pages.4929-4947
  • /
  • 2017
  • /
  • 1976-7277(pISSN)
  • /
  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
권호 표지
DOI QR코드

DOI QR Code

Structurally Enhanced Correlation Tracking

  • Parate, Mayur Rajaram (Visvesvaraya National Institute of Technology) ;
  • Bhurchandi, Kishor M. (Visvesvaraya National Institute of Technology)
  • 투고 : 2016.11.23
  • 심사 : 2017.05.14
  • 발행 : 2017.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

키워드

참고문헌

  1. Y. Wu, J. Lim, and M.-H. Yang, "Object tracking benchmark," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1834-1848, 2015. https://doi.org/10.1109/TPAMI.2014.2388226
  2. A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: an experimental survey," IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 36, no. 7, pp. 1442-1468, 2014. https://doi.org/10.1109/TPAMI.2013.230
  3. S. Lucey, "Enforcing non-positive weights for stable support vector tracking," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA, pp. 1-8, 2008.
  4. X. Wang, G. Hua, and T. X. Han, "Discriminative Tracking by Metric Learning," in Proc. of the European Conference on Computer Vision (ECCV), Berlin, Heidelberg, 2010.
  5. Z. Zuo, G. Wang, B. Shuai, L. Zhao, Q. Yang, and X. Jiang, "Learning discriminative and shareable features for scene classification," in Proc. of European Conference on Computer Vision (ECCV), Zurich,Switzerland., pp. 552-568, 2014.
  6. S. Avidan, "Ensemble Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 261-271, 2007. https://doi.org/10.1109/TPAMI.2007.35
  7. B. Babenko, M.-H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1619-1632, 2011. https://doi.org/10.1109/TPAMI.2010.226
  8. K. Junseok and L. Kyoung Mu, "Tracking by Sampling Trackers," in Proc. of IEEE International Conference on Computer Vision (ICCV) Barcelona, Spain, pp. 1195-1202, 2011.
  9. H. Weiming, L. Xi, L. Wenhan, Z. Xiaoqin, S. Maybank, and Z. Zhongfei, "Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 12, pp. 2420-2440, 2012. https://doi.org/10.1109/TPAMI.2012.42
  10. T. Liu, G. Wang, L. Wang, and K. L. Chan, "Visual Tracking via Temporally Smooth Sparse Coding," IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1452-1456, 2015. https://doi.org/10.1109/LSP.2014.2365363
  11. J. Xing, J. Gao, B. Li, W. Hu, and S. Yan, "Robust object tracking with online multi-lifespan dictionary learning," in Proc. of IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, pp. 665-672, 2013.
  12. K. Zhang, L. Zhang, and M.-H. Yang, "Real-time compressive tracking," in Proc. of European Conference on Computer Vision, pp. 864-877, 2012.
  13. S. Hare, A. Saffari, and P. H. Torr, "Struck: Structured output tracking with kernels," in Proc. of IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, pp. 263-270, 2011.
  14. Z. Kalal, J. Matas, and K. Mikolajczyk, "Pn learning: Bootstrapping binary classifiers by structural constraints," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, pp. 49-56, 2010.
  15. D. Chen, Z. Yuan, G. Hua, J. Wang, and N. Zheng, "Multi-timescale Collaborative Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-1, 2016. https://doi.org/10.1109/TPAMI.2016.2539956
  16. Z. Chen, Z. Hong, and D. Tao, "An Experimental Survey on Correlation Filter-based Tracking," arXiv preprint arXiv:1509.05520, 2015.
  17. D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, "Visual object tracking using adaptive correlation filters," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, pp. 2544-2550, 2010.
  18. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," in Proc. of European Conference on Computer Vision (ECCV) ed Florence, Italy: Springer Berlin Heidelberg, pp. 702-715, 2012.
  19. M. Danelljan, F. Shahbaz Khan, M. Felsberg, and J. Van de Weijer, "Adaptive color attributes for real-time visual tracking," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, pp. 1090-1097, 2014.
  20. Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark," in Proc. of IEEE Conference on Computer vision and pattern recognition (CVPR) Portland, OR, pp. 2411-2418, 2013.
  21. L. Cehovin, M. Kristan, and A. Leonardis, "An adaptive coupled-layer visual model for robust visual tracking," in Proc. of IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, pp. 1363-1370, 2011.
  22. D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental Learning for Robust Visual Tracking," International Journal of Computer Vision, vol. 77, no. 1-3, pp. 125-141, 2008/05/01 2008. https://doi.org/10.1007/s11263-007-0075-7
  23. G. Shu, A. Dehghan, O. Oreifej, E. Hand, and M. Shah, "Part-based multiple-person tracking with partial occlusion handling," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Rhode Island, pp. 1815-1821, 2012.
  24. B. Yang and R. Nevatia, "Online Learned Discriminative Part-Based Appearance Models for Multi-human Tracking," in Proc. of European Conference on Computer Vision (ECCV) A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, Eds., ed Florence, Heidelberg: Springer Berlin Heidelberg, pp. 484-498, 2012.
  25. X. Jia, H. Lu, and M.-H. Yang, "Visual tracking via adaptive structural local sparse appearance model," in Proc. of IEEE Conference on Computer vision and pattern recognition (CVPR), Providence, RI, pp. 1822-1829, 2012.
  26. T. Liu, G. Wang, and Q. Yang, "Real-time part-based visual tracking via adaptive correlation filters," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, pp. 4902-4912, 2015.
  27. Y. Li, J. Zhu, and S. C. Hoi, "Reliable patch trackers: Robust visual tracking by exploiting reliable patches," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, pp. 353-361, 2015.
  28. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, 2002. https://doi.org/10.1109/78.978374
  29. Y. Li and J. Zhu, "A scale adaptive kernel correlation filter tracker with feature integration," in Proc. of European Conference on Computer Vision (ECCV), Zurich, pp. 254-265, 2014.
  30. M. Danelljan, G. Hager, F. Khan, and M. Felsberg, "Accurate scale estimation for robust visual tracking," in Proc. of British Machine Vision Conference (BMVC) Nottingham, September 1-5, 2014.
  31. Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao, "Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, pp. 749-758, 2015.
  32. C. Ma, X. Yang, C. Zhang, and M.-H. Yang, "Long-term correlation tracking," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, Massachusetts, pp. 5388-5396, 2015.
  33. K. Zhang, L. Zhang, Q. Liu, D. Zhang, and M.-H. Yang, "Fast visual tracking via dense spatio-temporal context learning," in Proc. of European Conference on Computer Vision, Zurich, pp. 127-141, 2014.
  34. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-Speed Tracking with Kernelized Correlation Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 583-596, 2015. https://doi.org/10.1109/TPAMI.2014.2345390
  35. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 9, pp. 1627-1645, 2010. https://doi.org/10.1109/TPAMI.2009.167
  36. R. Rifkin, G. Yeo, and T. Poggio, "Regularized least-squares classification," Nato Science Series Sub Series III Computer and Systems Sciences, vol. 190, pp. 131-154, 2003.
  37. B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond: MIT press, 2002.
  38. Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-Learning-Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1409-1422, 2012. https://doi.org/10.1109/TPAMI.2011.239
  39. A. Adam, E. Rivlin, and I. Shimshoni, "Robust fragments-based tracking using the integral histogram," in Proc. of IEEE Conference on Computer vision and pattern recognition (CVPR), New York, NY, USA., pp. 798-805, 2006.
  40. K. Zhang, Q. Liu, Y. Wu, and M. H. Yang, "Robust Visual Tracking via Convolutional Networks Without Training," IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1779-1792, 2016. https://doi.org/10.1109/TIP.2016.2531283