Abstract
We propose a statistical motion model for sequential Bayesian tracking, called the optical flow–driven motion model, and show an adaptive particle filter algorithm with the motion model. It predicts the current state with the help of optical flows, i.e., it explores the state space with information based on the current and previous images of an image sequence. In addition, we introduce an automatic method for adjusting the variance of the motion model, which parameter is manually determined in most particle filters. In experiments with synthetic and real image sequences, we compare the proposed motion model with a random walk model, which is a widely used model for tracking, and show the proposed model outperform the random walk model in terms of accuracy even though their execution times are almost the same.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Doucet, A., de Freitas, N., Gordon, N.J.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Gordon, N.J., Salmond, D.J., Smith, A.F.M.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc.–F 140(2), 107–113 (1993)
Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5(1), 1–25 (1996)
Isard, M., Blake, A.: Condensation – Conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)
Haralick, R.M.: Propagating Covariance in Computer Vision. Int. J. Pattern Recognition and Artificial Intelligence 10(5), 561–572 (1996)
Nummiaro, K., Koller-Meier, E., Gool, L.V.: An adaptive color-based particle filter. Image and Vision Computing 21, 99–110 (2003)
Pitt, M.K., Shephard, N.: Filtering Via Simulation: Auxiliary Particle Filters. J. the American Statistical Association 94, 590–599 (1999)
Liu, J.S., Chen, R.: Sequential Monte Carlo methods for dynamical systems. J. the American Statistical Association 93(443), 1032–1044 (1998)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)
Kitagawa, G.: Self–organizing state space model. J. the American Statistical Association 93(443), 1203–1215 (1998)
Ichimura, N.: Stochstic Filtering for Motion Trajectory in Image Sequences Using a Monte Carlo Filter with Estimation of Hyper-Parameters. Proc. Int. Conf. on Pattern Recognition IV, 68–73 (2002)
Isard, M., Blake, A.: ICondensation: Unifying low-level and high-level tracking in a stochastic framework. Proc. European Conf. Computer Vision 1, 893–908 (1998)
Fischer, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conf., pp.147–151 (August 1988)
Shi, J., Tomasi, C.: Good Features to Track, Proc. Computer Vision Pattern Recognition, pp.593–600 (1994)
Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker, Intel Corporation, Microprocessor Research Labs (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kawamoto, K. (2007). Optical Flow–Driven Motion Model with Automatic Variance Adjustment for Adaptive Tracking. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-76386-4_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
eBook Packages: Computer ScienceComputer Science (R0)