Object tracking based on two-dimensional PCA

详细信息    查看全文

• 作者：Fuyuan Xu ; Guohua Gu ; Xiaofang Kong ; Pengcheng Wang ; Kan Ren
• 关键词：Object tracking ; Two ; dimensional PCA ; Appearance model ; Reliability ; Real ; time
• 刊名：Optical Review
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：23
• 期：2
• 页码：231-243
• 全文大小：3,121 KB
• 参考文献：1.Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 809–830 (2000)CrossRef
  2.Collins, R. T., Lipton, A. J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A system for video surveillance and monitoring. Technical report, VSAM final report, Carnegie Mellon University. Technical Report CMU-RI-TR-00-12 (2000)
  3.Javed, O., Shafique, K., Rasheed, Z., Shah, M.: Modeling inter-camera space–time and appearance relationships for tracking across non-overlapping views. Comput. Vis. Image Underst. 109(2), 146–162 (2008)CrossRef
  4.Li, X., Hu, W., Shen, C., et al.: A survey of appearance models in visual object tracking[J]. ACM Trans. Intell. Syst. Technol. (TIST) 4(4), 58 (2013)
  5.McKenna, S., Raja, Y., Gong, S.: Tracking colour objects using adaptive mixture models. Image Vis. Comput. 17, 223–229 (1999)CrossRef
  6.Wang, H., Suter, D., Schindler, K., Shen, C.: Adaptive object tracking based on an effective appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 29(9), 1661–1667 (2007)CrossRef
  7.Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1296–1311 (2003)CrossRef
  8.Wang, Q., Chen, F., Xu, W., Yang, M.: Object tracking via partial least squares analysis. IEEE Trans. Image Process. 21(10), 4454–4465 (2012)ADS MathSciNet CrossRef
  9.Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-euclidean riemannian subspace and block-division appearance model[J]. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2420–2440 (2012)CrossRef
  10.Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In European Conference on Computer Vision. pp. 234–247 (2008)
  11.Visentini I., Snidaro, L., Foresti, G.L.: Dynamic ensemble for target tracking. International Workshop on Visual Surveillance (2008)
  12.Yao, R., Shi, Q., Shen, C., Zhang, Y., Van Den Hengel, A.: Robust tracking with weighted online structured learning. European Conference on Computer Vision (2012)
  13.Yang, J., Zhang, D., Frangi, A.F., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J]. Pattern Anal. Mach. Intell. IEEE Trans. 26(1), 131–137 (2004)CrossRef
  14.Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using view-based representation. European Conference on Computer Vision, pp. 329–342 (1996)
  15.Ho, J., Lee, K., Yang, M., Kriegman, D.: Visual tracking using learned linear subspaces. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 782–789 (2004)
  16.Li, Y., Xu, L., Morphett, J., Jacobs, R.: On incremental and robust subspace learning. Pattern Recogn. 37(7), 1509–1518 (2004)CrossRef
  17.Skocaj, D. Leonardis, A.: Weighted and robust incremental method for subspace learning. In IEEE International Conference on Computer Vision, pp. 1494–1501 (2003)
  18.Levy, A., Lindenbaum, M.: Sequential Karhunen-Loeve basis extraction and its application to images[J]. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)ADS CrossRef MATH
  19.Zhang, C., Liu, R., Qiu, T., et al.: Robust visual tracking via incremental low-rank features learning[J]. Neurocomputing 131(7), 237–247 (2014)CrossRef
  20.Wang, D., Lu, H.: Object Tracking via 2DPCA and l1-Regularization[J]. Signal Process. Lett. IEEE 19, 711–714 (2012)ADS CrossRef
  21.Isard M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B, Cipolla, R. (eds.) LNCS. In: Proceedings of the Fourth European Conference on Computer Vision, vol. 1064, pp. 343–356. Springer, Berlin (1996)
  22.North, B., Blake, A.: Learning dynamical models using expectation-maximization. In Proceedings of IEEE international conference on computer vision, pp. 384–389 (1998)
  23.Ross, D.A., Lim, J., Lin, R.S., et al.: Incremental learning for robust visual tracking[J]. Int. J. Comput. Vision 77(1–3), 125–141 (2008)CrossRef
  24.Moghaddam, B., Pentland, A.: Probabilistic visual learning for object detection. In Proceedings of IEEE international conference on computer vision, pp. 786–793 (1995)
  25.Tipping, M.E., Bishop, C.M.: Mixtures of probabilistic principal component analyzers. Neural Comput. 11(2), 443–482 (1999)CrossRef
  26.Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In ICCV (2009)
  27.Kwon, J, Lee, K.: Visual Tracking Decomposition. In CVPR, pp. 1269–1276 (2010)
  28.Wang, T., Gu, I.Y.H., Shi, P.: Object Tracking using Incremental 2D-PCA Learning and ML Estimation. ICASSP 2007, pp. I-933–I-936 (2007)
  29.Wu, Y., Lim, J., Yang, M.H.: Object Tracking Benchmark[J]. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)CrossRef
  30.Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance Learning[J]. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)CrossRef
  31.Henriques, J.F., Rui, C., Martins, P. et al.: Exploiting the Circulant Structure of Tracking-by-Detection with Kernels[M]//Computer Vision—ECCV 2012. Springer, Berlin, pp. 702–715 (2012)
  32.Akgün, M.A., Garcelon, J.H., Haftka, R.T.: Fast exact linear and non-linear structural reanalysis and the Sherman–Morrison–Woodbury formulas[J]. Int. J. Numer. Meth. Eng. 50(7), 1587–1606 (2001). doi:10.​1002/​nme.​87 CrossRef MATH
  
• 作者单位：Fuyuan Xu (1)
  Guohua Gu (1)
  Xiaofang Kong (1)
  Pengcheng Wang (1)
  Kan Ren (1)
  
  1. Nanjing University of Science and Technology, Nanjing, Jiangsu, China
  
• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Electromagnetism, Optics and Lasers
  
• 出版者：The Optical Society of Japan, co-published with Springer-Verlag GmbH
• ISSN：1349-9432

文摘

In this paper, we present a novel object tracking method based on two-dimensional PCA. The low quality of images and the changes of the object appearance are very challenging for the object tracking. The representation of the training features is usually used to solve these challenges. Two-dimensional PCA (2DPCA) based on the image covariance matrix is constructed directly using the original image matrices. An appearance model is presented and its likelihood estimation has been established based on 2DPCA representation in this paper. Compared with the state-of-the-art methods, our method has higher reliability and real-time property. The performances of the proposed tracking method are quantitatively and qualitatively shown in experiments.