Abstract
A fast algorithm for the detection of independently moving objects by an also moving observer by means of investigating optical flow fields is presented. Since the measurement of optical flow is a computationally expensive operation, it is necessary to restrict the number of flow measurements. The proposed algorithm uses two different ways to determine the positions, where optical flow is calculated. A part of the positions is determined using a particle filter, while the other part of the positions is determined using a random variable, which is distributed according to an initialization distribution. This approach results in a restricted number of optical flow calculations leading to a robust real time detection of independently moving objects on standard consumer PCs.
This work was supported by BMBF Grant No. 1959156C.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barron, J.L., Fleet, D.J., Beauchemin, S.S., Burkitt, T.A.: Performance Of Optical Flow Techniques. In: Proc. CVPR, vol. 92, pp. 236–242 (1994)
Black, M.J., Fleet, D.J.: Probabilistic Detection and Tracking of Motion Discontinuities. In: ICCV (1999)
Förstner, W.: A feature based correspondence algorithm for image matching. International Archives of Photogrammetry and Remote Sensing 26-3/3, 150–166 (1986)
Gehrig, S., Wagner, S., Franke, U.: SystemArchitecture for an Intersection Assistant Fusing Image, Map and GPS Information. In: Proc. IEEE Intelligent Vehicles (2003)
Hue, C., Le Cardre, J.-P., Perez, P.: Tracking Multiple Objects with Particle Filtering. IEEE Transactions on Aerospace and Electronic Systems 38(3), 791–812 (2002)
Khan, Z., Balch, T., Dellaert, F.: An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 279–290. Springer, Heidelberg (2004)
Hartley, R., Zisserman, A.: Multiple View Geometry. Cambridge University Press, Cambridge (2000)
Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Isard, M., Blake, A.: ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)
Isard, M., McCormick, J.: BraMBLe: A Bayesian Multiple-Blob Tracker. In: ICCV (2001)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. DARPA IU Workshop, pp. 121–130 (1981)
Perez, P., et al.: Color-Based Probabilistic Tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Pollefeys, M., Koch, R., Van Gool, L.J.: Self-Calibration and Metric Reconstruction in Spite of Varying and Unknown Internal Camera Parameters. IJCV 32(1), 7–25 (1999)
Woelk, F., Gehrig, S., Koch, R.: A Monocular Image Based Intersection Assistant. IEEE Intelligent Vehicles, Parma, Italy (2004)
Vermaak, J., et al.: Maintaining Multi-Modality through Mixture Tracking. In: ICCV (2003)
Zelek, J.S.: Bayesian Real-time Optical Flow. In: Proc VI (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Woelk, F., Koch, R. (2004). Fast Monocular Bayesian Detection of Independently Moving Objects by a Moving Observer. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-28649-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
eBook Packages: Springer Book Archive