Abstract
We propose a novel, model-based approach for articulated object detection and pose estimation that does not need any low-level feature extraction or foreground segmentation and thus eliminates this error-prone step. Our approach works directly on the input color image and is based on a new kind of divergence of the color distribution between an object hypothesis and its background. Consequently, we get a color distribution of the target object for free.
We further propose a coarse-to-fine and hierarchical algorithm for fast object localization and pose estimation. Our approach works significantly better than segmentation-based approaches in cases where the segmentation is noisy or fails, e.g. scenes with skin-colored backgrounds or bad illumination that distorts the skin color.
We also present results by applying our novel approach to markerless hand tracking.
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Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., Wolf, H.C.: Parametric correspondence and chamfer matching: Two new techniques for image matching. In: International Joint Conference on Artificial Intelligence (1977)
Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transaction on Pattern Analysis and Machine Intelligence (1988)
Huttenlocher, D., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence (1993)
Athitsos, V., Sclaroff, S.: 3d hand pose estimation by finding appearance-based matches in a large database of training views. In: IEEE Workshop on Cues in Communication (2001)
Athitsos, V., Sclaroff, S.: An appearance-based framework for 3d hand shape classification and camera viewpoint estimation. In: IEEE Conference on Automatic Face and Gesture Recognition (2002)
Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: Boostmap: A method for efficient approximate similarity rankings. In: IEEE Conference on Computer Vision and Pattern Recognition (2004)
Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: IEEE International Conference on Computer Vision (1999)
Sudderth, E.B., Mandel, M.I., Freeman, W.T., Willsky, A.S.: Visual hand tracking using nonparametric belief propagation. In: IEEE CVPR Workshop on Generative Model Based Vision, vol. 12, p. 189 (2004)
Kato, M., Chen, Y.W., Xu, G.: Articulated hand tracking by pca-ica approach. In: International Conference on Automatic Face and Gesture Recognition, pp. 329–334 (2006)
Toyama, K., Blake, A.: Probabilistic tracking with exemplars in a metric space. International Journal of Computer Vision (2002)
Lin, Z., Davis, L.S., Doermann, D., DeMenthon, D.: Hierarchical part-template matching for human detection and segmentation. In: IEEE International Conference on Computer Vision (2007)
Olson, C.F., Huttenlocher, D.P.: Automatic target recognition by matching oriented edge pixels. IEEE Transactions on Image Processing (1997)
Thayananthan, A., Navaratnam, R., Stenger, B., Torr, P., Cipolla, R.: Pose estimation and tracking using multivariate regression. Pattern Recognition Letters (2008)
Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Hand pose estimation using hierarchical detection. In: International Workshop on Human-Computer Interaction (2004)
Shaknarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: IEEE International Conference on Computer Vision (2003)
Lin, J.Y., Wu, Y., Huang, T.S.: 3D model-based hand tracking using stochastic direct search method. In: International Conference on Automatic Face and Gesture Recognition, p. 693 (2004)
Wu, Y., Lin, J.Y., Huang, T.S.: Capturing natural hand articulation. In: International Conference on Computer Vision, vol. 2, pp. 426–432 (2001)
Ouhaddi, H., Horain, P.: 3D hand gesture tracking by model registration. In: Workshop on Synthetic-Natural Hybrid Coding and Three Dimensional Imaging, pp. 70–73 (1999)
Amai, A., Shimada, N., Shirai, Y.: 3-d hand posture recognition by training contour variation. In: IEEE Conference on Automatic Face and Gesture Recognition, pp. 895–900 (2004)
Shimada, N., Kimura, K., Shirai, Y.: Real-time 3-d hand posture estimation based on 2-d appearance retrieval using monocular camera. In: IEEE International Conference on Computer Vision, p. 23 (2001)
Zhou, H., Huang, T.: Okapi-chamfer matching for articulated object recognition. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1026–1033 (2005)
Zhou, H., Huang, T.: Tracking articulated hand motion with eigen dynamics analysis. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1102–1109 (2003)
Stenger, B., Thayananthan, A., Torr, P.H.S., Cipolla, R.: Model-based hand tracking using a hierarchical bayesian filter. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1372–1384 (2006)
Stenger, B.D.R.: Model-based hand tracking using a hierarchical bayesian filter. Dissertation submitted to the University of Cambridge (2004)
Wang, R.Y., Popović, J.: Real-time hand-tracking with a color glove. ACM Transactions on Graphics 28 (2009)
Mohr, D., Zachmann, G.: Fast: Fast adaptive silhouette area based template matching. In: Proceedings of the British Machine Vision Conference, pp. 39.1– 39.12. BMVA Press (2010), doi:10.5244/C.24.39
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Quasi-newton or variable metric methods in multidimensions. In: Numerical Recipes, The Art of Scientific Computing, pp. 521–526. Cambridge University Press, Cambridge (2007)
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Mohr, D., Zachmann, G. (2011). Segmentation-Free, Area-Based Articulated Object Tracking. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_11
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DOI: https://doi.org/10.1007/978-3-642-24028-7_11
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