Visual Target Tracking Using Geometrical Particle Filter and Analytic Color-Based Histogram Model
Subject Areas : electrical and computer engineeringN. Ghasemi 1 , P. Moallem 2 , M. F. Sabahi 3
1 -
2 -
3 -
Keywords: Visual tracking geometrical particle filter affine motion Lie group color-based histogram,
Abstract :
Color is an important feature to describe object in visual tracking. Color-based histogram is used to model the object properly and Bhattacharya distance is also used to measure the error between reference and candidate histogram. Particles filter estimate position of target while two-dimension affine transformation is used as state of the system. Considering geometric properties of affine transformation as affine group cause two-dimensional mapping of the object to be closer to the real three-dimensional model. Approximation of optimal importance function of particles filter is obtained from Taylor expansion of Bhattacharya distance. Experiments show the accuracy and stability of the proposed tracker for fast and complex movement of a color target versus the gray level geometric particle filtering algorithm.
[1] H. Yang, L. Shao, F. Zheng, L. Wang, and Z. Song, "Recent advances and trends in visual tracking: a review," Neurocomputing, vol. 74, no. 18, pp. 3823-3831, Nov. 2011.
[2] A. Yilmaz, O. Javed, and M. Shah, "Object tracking: a survey," ACM Comput. Surv., vol. 38, no. 4, pp. 13-57, Dec. 2006.
[3] J. Kwon, M. Choi, F. Park, and C. Chun, "Particle filtering on the euclidean group: framework and applications," Robotica, vol. 25, no. 6, pp. 725-737, Nov. 2007.
[4] W. Yan, C. Weber, and S. Wermter, "A hybrid probabilistic neural model for person tracking based on a ceiling-mounted camera," J. of Ambient Intelligence and Smart Environments, vol. 3, no. 3, pp. 237-252, Jul. 2011.
[5] S. Zhou, R. Chellappa, and B. Moghaddam, "Visual tracking and recognition using appearance-daptive models in particle filters," IEEE Trans. on Image Processing, vol. 13, no. 11, pp. 1491-1506, Nov. 2004.
[6] A. Doucet, S. Godsill, and C. Andrieu, "On sequential monte carlo sampling methods for Bayesian filtering," Stat. Comput., vol. 10, no. 3, pp. 197-208, Jul. 2000.
[7] R. Merwe, A. Doucet, N. Freitas, and E. Wan, "The unscented particle filter," Advances in Neural Information Processing Systems, vol. 13, pp. 584-590, Aug. 2001.
[8] X. Li, W. Hu, Z. Zhang, X. Zhang, and G. Luo, "Robust visual tracking based on incremental tensor subspace learning," in Proc. of the 11th IEEE Int. Conf. on Computer Vision, vol. 11, 8 pp., Oct. 2007.
[9] D. Ross, J. Lim, R. S. Lin, and M. H. Yang, "Incremental learning for robust visual tracking," International J. of Computer Vision, vol. 77, no. 1-3, pp. 125-141, May 2008.
[10] J. Kwon and F. Park, "Visual tracking via particle filtering on the affine group," The International Journal of Robotics Research, vol. 29, no. 2-3, pp. 198, 2010..
[11] J. Kwon, K. Mu Lee, and F. Park, "Visual tracking via geometric particle filtering on the affine group with optimal importance functions," in Proc. Computer Vision and Pattern Recognition, CVPR'09, pp. 991-998, Jun. 2009.
[12] C. Choi and H. I. Christensen, "Robust 3D visual tracking using particle filtering on the SE(3) group," in Proc. IEEE Conf. on Robotics and Automation, ICRA'11, pp. 4384-4390, May 2011.
[13] J. Xavier and J. Manton, "On the generalization of AR process to Riemannian manifolds," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, ICASSP'06, vol. 5, May 2006.
[14] S. Saha, P. Mandal, Y. Boers, and H. Driessen, "Gaussian proposal density using moment matching," Stat. Comput., vol. 19, no. 2, pp. 203-208, Jun. 2009.
[15] M. Li, T. Tan, W. Chen, and K. Huang, "Efficient object tracking by incremental self - tuning particle filtering on the affine group," IEEE Trans. on Image Processing, vol. 21, no. 3, pp. 1298-1313, Mar. 2012.
[16] Y. Lui, "Advances in matrix manifolds for computer vision," Image and Vision Computing, vol. 30, no. 6-7, pp. 380-388, Jun. 2012.
[17] Z. Khan, T. Balch, and F. Dellaert, "A Rao-Blackwellized particle filter for eigentracking," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'04, pp. 980-986, Jun. 2004.
[18] Z. Zivkovic and B. Krose, "An EM-like algorithm for color - histogram - based object tracking," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'04, pp. 798-803, Jun. 2004.
[19] J. Czyz, B. Ristic, and B. Macq, "A particle filter for joint detection and tracking of color objects," Image and Vision Computing, vol. 25, no. 8, pp. 1271-1281, Aug. 2007.
[20] ن. قاسمی، پ. معلم و م. ف. صباحی، "ردیابی تصویری به روش فیلتر ذرهای هندسی با استفاده از مدل هیستوگرام،" يازدهمين کنفرانس سيستمهاي هوشمند ايران، ICIS'13، اسفند 91.
[21] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, May 2003.