Abstract
The temporal changes of gray value structures recorded in an image sequence contain significantly more information about the recorded scene than the gray value structures of a single image. By incorporating optical flow estimates into the measurement function, our 3D pose estimation process exploits interframe information from an image sequence in addition to intraframe aspects used in previously investigated approaches. This increases the robustness of our vehicle tracking system and facilitates the correct tracking of vehicles even if their images are located in low contrast image areas. Moreover, partially occluded vehicles can be tracked without modeling the occlusion explicitly. The influence of interframe and intraframe image sequence data on pose estimation and vehicle tracking is discussed systematically based on various experiments with real outdoor scenes.
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Y. Bar-Shalom, T. E. Fortmann, Tracking and Data Association, Academic Press, Inc., Boston/MA, Orlando/FL, and others, 1988
J.L. Barron, D.J. Fleet, and S.S. Beauchemin, Performance of Optical Flow Techniques, International Journal of Computer Vision 12 (1994) 43–77
P. Bouthemy, E. FranÇois, Motion Segmentation and Qualitative Dynamic Scene Analysis from an Image Sequence, International Journal of Computer Vision 10 (1993) 157–182.
C. Cédras, M. Shah, Motion-Based Recognition: A Survey, Image and Vision Computing 13:2 (1995) 129–155.
A. Gelb (ed.), Applied Optimal Estimation, MIT Press, Cambridge/MA, 1974.
S. Gong, H. Buxton, From Contextual Knowledge to Computational Constraints, in Proc. British Machine Vision Conference, Guildford/UK, Sept. 21–23, 1993, pp. 229–238.
D. Koller, K. Daniilidis, H.-H. Nagel, Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes, International Journal of Computer Vision 10 (1993) 257–281.
H. Kollnig, H.-H. Nagel, and M. Otte, Association of Motion Verbs with Vehicle Movements Extracted from Dense Optical Flow Fields, in J.-O. Eklundh (ed.), Proc. Third European Conference on Computer Vision ECCV '94, Vol. II, Stockholm, Sweden, May 2–6, 1994, Lecture Notes in Computer Science 801, Springer-Verlag, Berlin, Heidelberg, New York/NY, and others, 1994, pp. 338–347.
H. Kollnig and H.-H. Nagel, 3D Pose Estimation by Fitting Image Gradients Directly to Polyhedral Models, Proc. Fifth International Conference on Computer Vision ICCV '95, Cambridge/MA, 20–23 June 1995, pp. 569–574
D.G. Lowe, Three-Dimensional Object Recognition from Single Two-Dimensional Images, Artificial Intelligence 31 (1987) 355–395.
M. Otte, H.-H. Nagel, Optical Flow Estimation: Advances and Comparisons, Proc. Third European Conference on Computer Vision ECCV '94, Vol. I, Stockholm / Sweden, 2–6 May 1994, J.-O. Eklundh (ed.), Lecture Notes in Computer Science 800, Springer-Verlag Berlin Heidelberg New York/NY 1994, pp. 51–60.
M. Otte, H.-H. Nagel, Estimation of Optical Flow Based on Higher-Order Spatiotemporal Derivatives in Interlaced and Non-Interlaced Image Sequences, Artificial Intelligence 78 (1995) 5–43
M. Proesmans, L. Van Gool, E. Pauwels, A. Oosterlinck, Determination of Optical Flow and Its Discontinuities Using Non-Linear Diffusion, in J.-O. Eklundh (Ed.), Proc. Third European Conference on Computer Vision ECCV '94, Vol. II, Stockholm, Sweden, May 2–6, 1994, Lecture Notes in Computer Science 801, Springer-Verlag, Berlin, Heidelberg, New York/NY and others, 1994, pp. 295–304.
J.R.J. Schirra, G. Bosch, C.K. Sung, G. Zimmermann, From Image Sequences to Natural Language: A First Step towards Automatic Perception and Description of Motion, Applied Artificial Intelligence 1 (1987) 287–307.
G.D. Sullivan, Visual Interpretation of Known Objects in Constrained Scenes, Philosophical Transactions Royal Society London (B) 337 (1992) 361–370.
G.D. Sullivan, A.D. Worrall, and J.M. Ferryman, Visual Object Recognition Using Deformable Models of Vehicles, in Proc. Workshop on Context-Based Vision, 19 June 1995, Cambridge/MA, pp. 75–86.
T.N. Tan, G.D. Sullivan, K.D. Baker, Fast Vehicle Localization and Recognition without Line Extraction and Matching, in Proc. British Machine Vision Conference, York/UK, Sept. 13–16, 1994, pp. 85–94.
A.D. Worrall, G.D. Sullivan, and K.D. Baker, Pose Refinement of Active Models Using Forces in 3D, in J.-O. Eklundh (ed.), Proc. Third European Conference on Computer Vision (ECCV '94), Vol. I, Stockholm, Sweden, May 2–6, 1994, Lecture Notes in Computer Science 800, Springer-Verlag, Berlin, Heidelberg, New York/NY, and others, 1994, pp. 341–350.
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Kollnig, H., Nagel, HH. (1996). Matching object models to segments from an optical flow field. In: Buxton, B., Cipolla, R. (eds) Computer Vision — ECCV '96. ECCV 1996. Lecture Notes in Computer Science, vol 1065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61123-1_155
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DOI: https://doi.org/10.1007/3-540-61123-1_155
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