Abstract
In this work, we propose a model-based approach for estimating the 3D position and orientation of a dummy’s head for crash test video analysis. Instead of relying on photogrammetric markers which provide only sparse 3D measurements, features present in the texture of the object’s surface are used for tracking. In order to handle also small and partially occluded objects, the concepts of region-based and patch-based matching are combined for pose estimation. For a qualitative and quantitative evaluation, the proposed method is applied to two multi-view crash test videos captured by high-speed cameras.
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References
Gall, J., Rosenhahn, B., Seidel, H.P.: Clustered stochastic optimization for object recognition and pose estimation. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 32–41. Springer, Heidelberg (2007)
Hogg, D.: Model-based vision: A program to see a walking person. Image and Vision Computing 1(1), 5–20 (1983)
Gavrila, D., Davis, L.: 3-d model-based tracking of humans in action: a multi-view approach. In: IEEE Conf. on Comp. Vision and Patt. Recog., pp. 73–80 (1996)
Bregler, C., Malik, J., Pullen, K.: Twist based acquisition and tracking of animal and human kinematics. Int. J. of Computer Vision 56(3), 179–194 (2004)
Rosenhahn, B., Brox, T., Weickert, J.: Three-dimensional shape knowledge for joint image segmentation and pose tracking. Int. Journal of Computer Vision 73(3), 243–262 (2007)
Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.P.: High accuracy optical flow serves 3-d pose tracking: Exploiting contour and flow based constraints. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 98–111. Springer, Heidelberg (2006)
Lepetit, V., Pilet, J., Fua, P.: Point matching as a classification problem for fast and robust object pose estimation. In: IEEE Conf. on Computer Vision and Patt. Recognition, pp. 244–250 (2004)
Li, H., Roivainen, P., Forcheimer, R.: 3-d motion estimation in model-based facial image coding. IEEE Trans. Pattern Anal. Mach. Intell. 15(6) (1993)
Gall, J., Rosenhahn, B., Seidel, H.P.: Robust pose estimation with 3d textured models. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 84–95. Springer, Heidelberg (2006)
Gehrig, S., Badino, H., Paysan, P.: Accurate and model-free pose estimation of small objects for crash video analysis. In: Britsh Machine Vision Conference (2006)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Conf. on Comp. Vision and Patt. Recog., pp. 593–600 (1994)
Lowe, D.: Object recognition from local scale-invariant features. In: Int. Conf. on Computer Vision, pp. 1150–1157 (1999)
Ke, Y., Sukthankar, R.: Pca-sift: A more distinctive representation for local image descriptors. In: IEEE Conf. on Comp. Vision and Patt. Recog., vol. 2, pp. 506–513 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: IEEE Conf. on Computer Vision and Patt. Recognition, pp. 257–263 (2003)
Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. Journal of Computer Vision 13(2), 119–152 (1994)
Stolfi, J.: Oriented Projective Geometry: A Framework for Geometric Computation. Academic Press, Boston (1991)
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Gall, J., Rosenhahn, B., Gehrig, S., Seidel, HP. (2008). Model-Based Motion Capture for Crash Test Video Analysis. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_10
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DOI: https://doi.org/10.1007/978-3-540-69321-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
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