Abstract
This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linear subspace models or Dynamic Point Distribution Models (DPDMs), which can deal with both continuous and discontinuous appearance changes; the representation is learned fully automatically from training data. The representation is enriched with texture information by means of intensity histograms, which are compared using the Bhattacharyya coefficient. Direct 3D measurement is furthermore provided by a stereo system.
State propagation is achieved by a particle filter which combines the three cues shape, texture and depth, in its observation density function. The tracking framework integrates an independently operating object detection system by means of importance sampling. We illustrate the benefit of our integrated multi-cue tracking approach on pedestrian tracking from a moving vehicle.
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References
Bar-Shalom, Y., Li, X.R., Kirubarajan, T. (eds.): Estimation with applications to tracking and navigation. Wiley, Chichester (2001)
Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)
Cootes, T.F., Taylor, C.J., Cooper, D.C., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Doucet, A., de Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Elliot, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models, 2nd edn. Springer, Heidelberg (1997)
Gavrila, D.M.: Multi-feature hierarchical template matching using distance transforms. In: Proc. of the ICPR, Brisbane, pp. 439–444 (1998)
Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000)
Gavrila, D.M., Giebel, J.: Virtual sample generation for template-based shape matching. In: Proc. of the IEEE CVPR Conf., pp. I:676–681 (2001)
Heap, T., Hogg, D.: Wormholes in shape space: Tracking through discontinuous changes in shape. In: Proc. of the ICCV, pp. 344–349 (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., Blake, A.: A mixed-state condensation tracker with automatic modelswitching. In: Proc. of the ICCV, pp. 107–112 (1998)
Isard, M., MacCormick, J.: Bramble: A bayesian multiple-blob tracker. In: Proc. of the ICCV, pp. 34–41 (2001)
Julier, S., Uhlmann, J.: A new extension of the kalman filter to nonlinear systems. In: Int. Symp. Aerospace/Defense Sensing, Simul. and Controls (1997)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object tracking with an adaptive color-based particle filter. In: Proc. of the Deutsche Arbeitsgemeinschaft für Mustererkennung, Zurich, Switzerland (2002)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: 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)
Rubner, Y., Puzicha, J., Tomasi, C., Buhmann, J.M.: Empirical evaluation of dissimilarity measures for color and texture. CVIU 84(1), 25–43 (2001)
Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Machine, Vision and Applications 14, 50–58 (2003)
Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. Int. J. of Computer Vision 48(1), 9–19 (2002)
Triesch, J., von der Malsburg, C.: Self-organized integration of adaptive visual cues for face tracking. In: Proc. of the IEEE Int. Conf. on Automatic Face and Gesture Recognition, Los Alamitos (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuro Science 3(1), 71–86 (1991)
Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: Proc. of the ICCV (2003)
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Giebel, J., Gavrila, D.M., Schnörr, C. (2004). A Bayesian Framework for Multi-cue 3D Object Tracking. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24673-2_20
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DOI: https://doi.org/10.1007/978-3-540-24673-2_20
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