Table of Contents Author Guidelines Submit a Manuscript
Advances in Multimedia
Volume 2018, Article ID 7481645, 6 pages
https://doi.org/10.1155/2018/7481645
Research Article

Visual Tracking Based on Discriminative Compressed Features

1Department of Modern Education Technology, Ludong University, Yantai, China
2Lab, CNCERT/CC, Yumin Road No. 3A, Beijing 100029, China

Correspondence should be addressed to Wei Liu; moc.anis@wludl

Received 3 April 2018; Revised 13 June 2018; Accepted 11 July 2018; Published 1 August 2018

Academic Editor: Lei Zhang

Copyright © 2018 Wei Liu and Hui Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. B. Dinh, N. Vo, and G. Medioni, “Context tracker: exploring supporters and distracters in unconstrained environments,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 1177–1184, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “Exploiting the circulant structure of tracking-by-detection with kernels,” in Proceedings of the European Conference on Computer Vision, pp. 702–715, 2012.
  3. J. Kwon and K. M. Lee, “Tracking by sampling trackers,” in Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV '11), pp. 1195–1202, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Han and P. H. N. De With, “Real-time multiple people tracking for automatic group-behavior evaluation in delivery simulation training,” Multimedia Tools and Applications, vol. 51, no. 3, pp. 913–933, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Han, Q. Ye, and J. Jiao, “Combined feature evaluation for adaptive visual object tracking,” Computer Vision and Image Understanding, vol. 115, no. 1, pp. 69–80, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. Han, J. Jiao, B. Zhang, Q. Ye, and J. Liu, “Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR),” Pattern Recognition, vol. 44, no. 9, pp. 2170–2183, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Han, E. J. Pauwels, P. M. De Zeeuw, and P. H. N. De With, “Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment,” IEEE Transactions on Consumer Electronics, vol. 58, no. 2, pp. 255–263, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Gao, Z. Han, C. Li, Q. Ye, and J. Jiao, “Real-Time Multipedestrian Tracking in Traffic Scenes via an RGB-D-Based Layered Graph Model,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2814–2825, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Zhang, W. Wu, T. Chen, N. Strobel, and D. Comaniciu, “Robust object tracking using semi-supervised appearance dictionary learning,” Pattern Recognition Letters, vol. 62, pp. 17–23, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Zhang, H. Zhou, H. Yao, Y. Zhang, K. Wang, and J. Zhang, “Adaptive NormalHedge for robust visual tracking,” Signal Processing, vol. 110, pp. 132–142, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Zhang, S. Kasiviswanathan, P. C. Yuen, and M. Harandi, “Online dictionary learning on symmetric positive definite manifolds with vision applications,” in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3165–3173, January 2015. View at Scopus
  12. Z. He, X. Li, X. You, D. Tao, and Y. Y. Tang, “Connected component model for multi-object tracking,” IEEE Transactions on Image Processing, vol. 25, no. 8, pp. 3698–3711, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. X. Li, Q. Liu, Z. He, H. Wang, C. Zhang, and W.-S. Chen, “A multi-view model for visual tracking via correlation filters,” Knowledge-Based Systems, vol. 113, pp. 88–99, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Qi, S. Zhang, L. Qin et al., “Hedged deep tracking,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 4303–4311, July 2016. View at Scopus
  15. Z. He, S. Yi, Y.-M. Cheung, X. You, and Y. Y. Tang, “Robust Object Tracking via Key Patch Sparse Representation,” IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 354–364, 2017. View at Google Scholar · View at Scopus
  16. R. Shi, J. Zhang, Z. Xie, J. Gao, and X. Zheng, “Robust tracking with per-exemplar support vector machine,” IET Computer Vision, vol. 9, no. 5, pp. 699–710, 2015. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Wilf, S. Zhang, S. Chikkerur, S. A. Little, S. L. Wing, and T. Serre, “Computer vision cracks the leaf code,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 113, no. 12, pp. 3305–3310, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Liu, Z. Lin, L. Shao, F. Shen, G. Ding, and J. Han, “Sequential discrete hashing for scalable cross-modality similarity retrieval,” IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 107–118, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. Y. Guo, G. Ding, L. Liu, J. Han, and L. Shao, “Learning to hash with optimized anchor embedding for scalable retrieval,” IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1344–1354, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  20. S. Zhang, H. Yao, X. Sun et al., “Action recognition based on overcomplete independent components analysis,” Information Sciences, vol. 281, pp. 635–647, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Jiang, S. Zhang, S. Wu, Y. Gao, and D. Zhao, “Multi-layered gesture recognition with Kinect,” Journal of Machine Learning Research (JMLR), vol. 16, pp. 227–254, 2015. View at Google Scholar · View at MathSciNet
  22. K. Chen, G. Ding, and J. Han, “Attribute-based supervised deep learning model for action recognition,” Frontiers of Computer Science, vol. 11, no. 2, pp. 219–229, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Zhang, X. Lan, H. Yao, H. Zhou, D. Tao, and X. Li, “A biologically inspired appearance model for robust visual tracking,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2357–2370, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. S. Zhang, X. Lan, Y. Qi, and P. C. Yuen, “Robust Visual Tracking via Basis Matching,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 3, pp. 421–430, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Lan, S. Zhang, and P. C. Yuen, “Robust joint discriminative feature learning for visual tracking,” in Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 3403–3410, July 2016. View at Scopus
  26. S. Zhang, H. Zhou, F. Jiang, and X. Li, “Robust visual tracking using structurally random projection and weighted least squares,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 11, pp. 1749–1760, 2015. View at Publisher · View at Google Scholar
  27. S. Zhang, H. Yao, X. Sun, and X. Lu, “Sparse coding based visual tracking: review and experimental comparison,” Pattern Recognition, vol. 46, no. 7, pp. 1772–1788, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. S. H. Zhang, H. Yao, H. Zhou, X. Sun, and S. H. Liu, “Robust visual tracking based on online learning sparse representation,” Neurocomputing, vol. 100, pp. 31–40, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Zhang, H. Yao, X. Sun, and S. Liu, “Robust visual tracking using an effective appearance model based on sparse coding,” ACM Transactions on Intelligent Systems and Technology, vol. 3, no. 3, pp. 43:1–43:18, 2012. View at Google Scholar · View at Scopus
  30. M. Yakut and N. Kehtarnavaz, “Ice-hockey puck detection and tracking for video highlighting,” Signal, Image and Video Processing, vol. 10, no. 3, pp. 527–533, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. I. S. Topkaya, H. Erdogan, and F. Porikli, “Tracklet clustering for robust multiple object tracking using distance dependent Chinese restaurant processes,” Signal, Image and Video Processing, vol. 10, no. 5, pp. 795–802, 2016. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Wang and Q. Zhao, “Robust object tracking via online Principal Component–Canonical Correlation Analysis (P3CA),” Signal, Image and Video Processing, vol. 9, no. 1, pp. 159–174, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Shan and C. Zhang, “Visual tracking using IPCA and sparse representation,” Signal, Image and Video Processing, vol. 9, no. 4, pp. 913–921, 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. Y. Qi, S. Zhang, L. Lei Qin et al., “Hedging Deep Features for Visual Tracking,” in Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2018. View at Publisher · View at Google Scholar
  35. J. Sembiring, A. S. Sabzevary, and K. Akizuki, “Stochastic process on multiwavelet,” IFAC Proceedings Volumes, vol. 35, no. 1, pp. 211–215, 2002. View at Google Scholar
  36. Z. Kalal, J. Matas, and K. Mikolajczyk, “P-N learning: bootstrapping binary classifiers by structural constraints,” in Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 49–56, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Hare, A. Saffari, and P. H. S. Torr, “Struck: structured output tracking with kernels,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 263–270, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. C. Bao, Y. Wu, H. Ling, and H. Ji, “Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach,” in Proceedings of the IIEEE Conference on Computer Vision and Pattern Recognition, pp. 1830–1837, June 2012. View at Scopus
  39. T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, “Robust visual tracking via multi-task sparse learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012.