1. 1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Image net classification with deep convolutional neural networks,” in Proc. NIPS, 2012,pp. 1097– 1105.
   2. Q. V. Le, W. Y. Zou, S. Y. Yeung, and A. Y. Ng, “Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis,” in Proc. IEEE Conf. CVPR, Jun. 2011,pp. 3361–3368
   3. G. Hinton et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012.
   4. N. Wang and D.-Y. Yeung, “Learning a deep compact image representation for visual tracking,” in Proc. NIPS, 2013, pp. 809–817.
   5. C. F. Cadieu and B. A. Olshausen, “Learning transformational invariants from natural movies,” in Proc. NIPS, 2008, pp. 209–216.
   6. W. Y. Zou, A. Y. Ng, S. Zhu, and K. Yu, “Deep learning of invariant features via simulated fixations in video,” in Proc. NIPS, 2012,pp. 3212– 3220.
   7. J. Nocedal, “Updating quasi-Newton matrices with limited storage,”Math. Comput., vol. 35, no. 151, pp. 773–782, 1980.
   8. J. Lu, G. Wang, and P. Moulin, “Human identity and gender recognition from gait sequences with arbitrary walking directions,” IEEE Trans. Inf. Forensics Security, vol. 9, no. 1, pp. 51– 61, Jan. 2014
   9. B. Wang, G. Wang, K. L. Chan, and L. Wang, “Tracklet association with online target-specific metric learning,” in Proc. IEEE Conf. CVPR, Jun. 2014, pp. 1234–1241.
   10. M. J. Black and A. D. Jepson, “EigenTracking: Robust matching and tracking of articulated objects using a view-based representation,” Int. J. Comput. Vis., vol. 26, no. 1, pp. 63–84, Jan. 1998.
   11. J. Ho, K.-C. Lee, M.-H. Yang, and D. Kriegman, “Visual tracking using learned linear subspaces,” in Proc. IEEE Conf. CVPR, Jun./Jul. 2004, pp. I-782–I-789.
   12. D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” Int. J. Comput. Vis., vol. 77, nos. 1–3, pp. 125–141, May 2008.
   13. M. Isard and A. Blake, “CONDENSATION— Conditional density propagation for visual tracking,” Int. J. Comput. Vis., vol. 29, no. 1, pp. 5– 28, Aug. 1998
   14. A. Doucet, N. de Freitas, and N. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice. Berlin, Germany: Springer-Verlag, 2001.
   15. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564– 577, May 2003.
   16. D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603–619, May 2002.
   17. S. Baker and I. Matthews, “Lucas–Kanade 20 years on: A unifying framework,” Int. J. Comput. Vis., vol. 56, no. 3, pp. 221–255, Feb. 2004.
   18. A. D. Jepson, D. J. Fleet, and T. F. ElMaraghi, “Robust online appearance models for visual tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 10, pp. 1296–1311, Oct. 2003
   19. G. Wang, D. Hoiem, and D. Forsyth, “Learning image similarity from Flickr groups using fast kernel machines,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2177–2188, Nov. 2012.
   20. S. Avidan, “Support vector tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 8, pp. 1064–1072, Aug. 2004.
   21. S. Avidan, “Ensemble tracking,” in Proc. IEEE Conf. CVPR, Sep. 2005, pp. 494–501