Abstract
In path based filtering the usual state space is replaced by a finite dimensional approximation to the path space of the system. The information in the system model and the measurements is summarised by a probability density function on the approximating space. Path based filters are well suited to the inference of system behaviour over time. They have the advantage that the predicted or estimated paths are always physically plausible, in that they are realisations of the system model. The filter is applied to the model based tracking of cars. Measurements of the position and orientation of a moving car are obtained by fitting a wire frame model to a sequence of video images. The filter estimates the velocity, acceleration and steering angle of the car. Experiments show that the steering angle can be estimated after tracking for 1 s and the acceleration after 2 s.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Mathematics in Science and Engineering Series 179. Academic Press: San Diego, CA (1988).
Blake, A., Zisserman, A.: Visual Reconstruction. MIT Press (1987).
Bolza, O.: Lectures on the Calculus of Variations. New York: Dover Publications Inc (1961).
Heisler, H.: Advanced Vehicle Technology. Hodder and Stoughton (1989).
Hull, J.: An Introduction to Futures and Options Markets. Prentice Hall International Editions (2nd edition, 1995).
Kee, R.J., Irwin, G.W.: Investigation of trellis based filters for tracking. IEE Proc. Radar, Sonar and Navigation 141 (1994) 9–18.
Maybank, S.J., Worrall, A.D., Sullivan, G.D.: A filter for visual tracking based on a stochastic model for driver behaviour. In Buxton, B., Cipolla, R. (eds.) Computer Vision-ECCV'96, Lecture Notes in Computer Science 1065 540–549. SpringerVerlag: Berlin, Heidelberg, New York (1996).
Maybank, S.J., Worrall, A.D., Sullivan, G.D.: Filter for car tracking based on acceleration and steering angle. In Fisher, R.B., Trucco, E. (eds.) British Machine Vision Conf. 1996, 2 (1996) 615–624.
Maybeck, P.S.: Stochastic Models, Estimation and Control — Volume 1. Mathematics in Science and Engineering Series 141-1. San Diego, CA, USA: Academic Press (1979).
Maybeck, P.S.: Stochastic Models, Estimation and Control — Volume 3. Mathematics in Science and Engineering Series 141-3. London, UK: Academic Press (1982).
Øksendal, B.: Stochastic Differential Equations: an introduction with applications. Springer-Verlag: Berlin, Heidelberg, New York (Third edition 1992).
Press, W.H. et al. Numerical Recipes. Cambridge University Press (1986).
Stroock, D.W.: Gaussian measures in traditional and not so traditional settings. Bulletin of the American Mathematical Society 33 (1996) 135–155.
Sullivan, G.D.: Model-based vision for traffic scenes using the ground plane constraint. In Terzopoulos, D., Brown,C.: (eds) Real-time Computer Vision. Cambridge University Press (1994).
Wolfram, S.: The Mathematica book. Cambridge University Press (Third edition 1996).
Worrall, A.D., Sullivan, G.D., Baker, K.D.: A simple, intuitive camera calibration tool for natural images. In Hancock, E.: (ed.) British Machine Vision Conf. 1994, 2 (1994) 781–790.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maybank, S.J., Worrall, A.D. (1997). Path prediction and classification based on non-linear filtering. In: Sommer, G., Koenderink, J.J. (eds) Algebraic Frames for the Perception-Action Cycle. AFPAC 1997. Lecture Notes in Computer Science, vol 1315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017876
Download citation
DOI: https://doi.org/10.1007/BFb0017876
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63517-8
Online ISBN: 978-3-540-69589-9
eBook Packages: Springer Book Archive