Abstract
We introduce the concept of a spline resampling in the particle filter to deal with the high accuracy and the sample impoverishment. The resampling is usually based on a linear transformation on the weights of the particles, so it affects the accurate filtering. The spline resampling consists of two parts: the spline transformation of weights and the spread transformation of states. The former is based on a spline transformation on the weights of the particles to obtain the high accuracy of particle filtering, and the latter is based on a point spread transformation on states of particles to prevent the sample impoverishment due to decline of the diversity of hypothesis after resampling. Two transformations are sequentially implemented to incorporate with each other. Then, we propose a global transition model in the particle filter, which takes account of the background variation caused by the camera motion model of object itself, to decrease error from real object position. We test the performance of our spline resampling and the global transition model in the particle filter in object tracking scenario. Experimental results demonstrate that particle filter with the spline resampling and the global transition model has the promising discriminative capability in comparison with other ones.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Comaniciu D, Ramesh V, Meer P (2003) Kernel based object tracking. IEEE Trans Pattern Anal Machine Intell 25(5):564–577
Douc R, Cappe O (2005) Comparison of resampling schemes for particle filtering.In: Proceedings of the 4th international symposium on in image and signal processing and analysis, pp 64–69
Fu XY, Jia YM (2010) An improvement on resampling algorithm of particle filters. IEEE Trans Signal Processing 58(10):5414–5420
Gilks WR, Berzuini C (2001) Following a moving target-Monte Carlo inference for dynamic Bayesian models. J R Stat Soc Ser B 63:127–146
Gordon N, Salmond D (1993) Novel approach to non-linear and non-Gaussian Bayesian state estimation. J Proc Inst Electric Eng 140(2): 107–113
Hearn D, Baker MP (1997) Computer graphics. Prentice Hall, New Jersey
Isard M, Blake A (1998) Condensation conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28
Kotecha JH, Djuric PM (2003) Gaussian particle filtering. J Signal Process IEEE Trans 51(10):259–260
Liang J, Peng XY (2008) Improved particle filter for nonlinear system state. J IEEE Electron Lett 44(21):1275–1277
Liang J, Peng XY (2010) Development and prospect of particle filter. Recent Patents on Electrical Engineering 3:43–44
Liang J, Peng XY, Ma YT (2008) Particle estimation algorithm using correlation of observation for nonlinear system state. J IEEE Electron Lett 44(8):553–554
Musso C, Oudjane N, LeGland F Doucet A, de Freitas JFG, Gordon NJ (eds) (2001) Improving regularized particle filters. Springer, New York
Pitt M, Shephard N (1999) Filtering via simulation: auxiliary particle filter. J Am Stat Assoc 94(446):590–599
Sanjeev M, Maskell S (2002) A tutorial on particle filter for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Processing 50(2):174–188
Wu G, Tang Z (2009) A new resampling strategy about particle filter algorithm applied in Monte Carlo frameworkIn: Proceedings of second international conference on intelligent computation technology and automation. Hunan, pp 507–510
Yao A, Lin X, Wang G, Yu S (2012) A compact association of particle filtering and kernel based object tracking. Pattern Recogn 45:2584–2597
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Choe, G., Wang, T., Liu, F. et al. Visual tracking based on particle filter with spline resampling. Multimed Tools Appl 74, 7195–7220 (2015). https://doi.org/10.1007/s11042-014-1960-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-014-1960-z