Abstract
In motion science, biology and robotics animal movement analyses are used for the detailed understanding of the human bipedal locomotion. For this investigations an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks, for example bone ends, have to be located and annotated in each image. In this paper we show a reduction of this effort by automating the annotation with a minimum level of user interaction. Recent approaches, based on Active Appearance Models, are improved by priors based on anatomical knowledge and an online tracking method, requiring only a single labeled frame. In contrast, we propose a one-shot learned tracking-by-detection prior which overcomes the shortcomings of template drifts without increasing the number of training data. We evaluate our approach based on a variety of real-world X-ray locomotion datasets and show that our method outperforms recent state-of-the-art concepts for the task at hand.
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References
Nyakatura, J.A., Andrada, E., Blickhan, R., Fischer, M.S.: Avian bipedal locomotion. In: 5th International Symposium on Adaptive Motion of Animals and Machines (AMAM). Elsevier (2011)
Andrada, E., Nyakatura, J.A., Bergmann, F., Blickhan, R.: Adjustments of global and local hindlimb properties during terrestrial locomotion of the common quail (coturnix coturnix). J. Exp. Biol. (2013)
Sigal, L., Balan, A.O., Black, M.J.: HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int. J. Comput. Vis. 87(1–2), 4 (2010)
Haase, D., Andrada, E., Nyakatura, J.A., Kilbourne, B.M., Denzler, J.: Automated approximation of center of mass position in X-ray sequences of animal locomotion. J. Biomech. 46, 2082–2086 (2013)
Haase, D., Denzler, J.: 2D and 3D analysis of animal locomotion from biplanar X-ray videos using augmented active appearance models. EURASIP J. Image Video Process. (2013)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. (2001)
Amthor, M., Haase, D., Denzler, J.: Fast and robust landmark tracking in X-ray locomotion sequences containing severe occlusions. In: International Workshop on Vision, Modelling, and Visualization (VMV). Eurographics Association (2012)
Mothes, O., Denzler, J.: Anatomical landmark tracking by one-shot learned priors for augmented active appearance models. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 246–254 (2017)
Haase, D., Denzler, J.: Anatomical landmark tracking for the analysis of animal locomotion in X-ray videos using active appearance models. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 604–615. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_56
Haase, D., Nyakatura, J.A., Denzler, J.: Multi-view active appearance models for the X-ray based analysis of avian bipedal locomotion. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 11–20. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23123-0_2
Lelieveldt, B., Üzümcü, M., van der Geest, R., Reiber, J., Sonka, M.: Multi-view active appearance models for consistent segmentation of multiple standard views: application to long- and short-axis cardiac MR images. In: International Congress Series (2003)
Amthor, M., Haase, D., Denzler, J.: Robust pictorial structures for X-ray animal skeleton tracking. In: International Conference on Computer Vision Theory and Applications (VISAPP). SCITEPRESS (2014)
Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2010)
Li, L., Nawaz, T., Ferryman, J.: Pets 2015: datasets and challenge. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE (2015)
Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE (2008)
Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011)
Jiang, X., Haase, D., Körner, M., Bothe, W., Denzler, J.: Accurate 3D multi-marker tracking in X-ray cardiac sequences using a two-stage graph modeling approach. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 117–125. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40246-3_15
Dehghan, A., Modiri Assari, S., Shah, M.: GMMCP tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. IEEE (2005)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Breiman, L.: Classification and Regression Trees. CRC Press, Boca Raton (2017)
Hariharan, B., Malik, J., Ramanan, D.: Discriminative decorrelation for clustering and classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 459–472. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_33
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE (2013)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0054760
Kendall, D.G.: Shape manifolds, procrustean metrics, and complex projective spaces. Bull. London Math. Soc. 16, 81–121 (1984)
Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics. Springer, Heidelberg (2002). https://doi.org/10.1007/b98835
Berg, M., Cheong, O., Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77974-2
Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)
Freytag, A., Schadt, A., Denzler, J.: Interactive image retrieval for biodiversity research. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 129–141. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24947-6_11
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)
Bellman, R.: On a routing problem. Q. Appl. Math. 16(1), 87–90 (1958)
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The research was supported by grant DE 735/8-3 of the German Research Foundation (DFG).
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Mothes, O., Denzler, J. (2019). One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics – Theory and Applications. VISIGRAPP 2017. Communications in Computer and Information Science, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-12209-6_5
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DOI: https://doi.org/10.1007/978-3-030-12209-6_5
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