[1] Gao Jianpo, Yang Hao, An Guocheng, Wu Zhenyang, et al. Face tracking algorithm based on particle filterwith mean shift importance sampling [J]. Journal of Southeast University (English Edition), 2007, 23 (2): 196-201. [doi:10.3969/j.issn.1003-7985.2007.02.009]

Copy

Face tracking algorithm based on particle filterwith mean shift importance sampling()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

23

Issue:

2007 2

Page:

196-201

Research Field:

Computer Science and Engineering

Publishing date:

2007-06-30

Info

Title:

Face tracking algorithm based on particle filterwith mean shift importance sampling

Author(s):

Gao Jianpo;  Yang Hao;  An Guocheng;  Wu Zhenyang

School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

face tracking;  particle filter;  importance sampling;  condensation;  mean shift

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2007.02.009

Abstract:

The condensation tracking algorithm uses a prior transition probability as the proposal distribution, which does not make full use of the current observation.In order to overcome this shortcoming, a new face tracking algorithm based on particle filter with mean shift importance sampling is proposed.First, the coarse location of the face target is attained by the efficient mean shift tracker, and then the result is used to construct the proposal distribution for particle propagation.Because the particles obtained with this method can cluster around the true state region, particle efficiency is improved greatly.The experimental results show that the performance of the proposed algorithm is better than that of the standard condensation tracking algorithm.

References:

[1] Maskell S, Gordon S, Clapp N.A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking [J].IEEE Transactions on Signal Processing, 2002, 50(2):174-188.
[2] Merwe R, Doucet A, Freitas N, et al.The unscented particle filter, CUED/FINFENG/TR 380 [R].Cambridge:Cambridge University, 2000.
[3] Doucet A, Godsill S, Andrieu C.On sequential Monte Carlo sampling methods for Bayesian filtering [J].Statistics and Computing, 2000, 10(3):197-208.
[4] Haykin S, Huber K, Chen Z.Bayesian sequential state estimation for MIMO wireless communication [J].Proceedings of the IEEE, 2004, 92(3):439-454.
[5] Kotecha J H, Djuric P M.Gaussian particle filtering [J].IEEE Transactions on Signal Processing, 2003, 51(10):2592-2601.
[6] Kotecha J H, Djuric P M.Gaussian sum particle filtering [J].IEEE Transactions on Signal Processing, 2003, 51(10):2602-2612.
[7] Isard M, Blake A.Condensation-conditional density propagation for visual tracking [J].International Journal of Computer Vision, 1998, 29(1):5-28.
[8] Comaniciu D, Ramesh V, Meer P.Kernel-based object tracking [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5):564-577.
[9] Comaniciu D, Meer P.Mean shift:a robust approach toward feature space analysis [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.
[10] Isard M, Blake A.ICONDENSATION:unifying low-level and high-level tracking in a stochastic framework [C]//Proceeding of the 5th European Conference on Computer Vision. Freiburg, Germany, 1998, 1:893-908.

Memo

Memo:

Biographies: Gao Jianpo(1975—), male, graduate;Wu Zhenyang(corresponding author), male, professor, zywu@seu.edu.cn.

Common functions