Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

VIDEO TRACKING FOR MULTI TASK BY USING HIERARCHICAL FEATURES

Author : N.Latha 1 N.Ritesh 2 N. Pushpalatha 3

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Date of Publication :7th March 2016

Abstract: To learn the hierarchical features for visual object tracking is the capability of handling complicated motion transformations. In this first learned the offline features as robust to diverse motion patterns from the auxiliary video sequences. The hierarchical features are learned into two-layer convolution neural network, which are important for visual object tracking. The target of the video sequence is used to be domain adaption module to learn online adapt with the pre-learned features according to the specific target object. The adaption is containing the both layers of deep learning features and robust to complicated motion transformation. That capture the changes for specific target objects to learn online adapt with pre-learned generic features used to test video sequence. It will be integrate our feature learning algorithm into three methods. They demonstrate that significant improvements and can be achieved by using learned hierarchical features, especially on the video sequences with complicated motion transformations and usually requires a lot of training data to learn deep structure and its parameters.

Reference :

    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

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