Abstract
This paper presents a method to create a model of an articulated object using the planar motion in an initialization video. The model consists of rigid parts connected by points of articulation. The rigid parts are described by the positions of salient feature-points tracked throughout the video. Following a filtering step that identifies points that belong to different objects, rigid parts are found by a grouping process in a graph pyramid. Valid articulation points are selected by verifying multiple hypotheses for each pair of parts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, J.K., Cai, Q.: Human motion analysis: A review. Computer Vision and Image Understanding 73(3), 428–440 (1999)
Aggarwal, J.K., Cai, Q., Liao, W., Sabata, B.: Articulated and elastic non-rigid motion: A review. In: IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 2–14 (1994)
Altunbasak, Y., Eren, P.E., Tekalp, A.M.: Region-based parametric motion segmentation using color information. Graphical Models and Image Processing 60(1), 13–23 (1998)
Artner, N.M., Ion, A., Kropatsch, W.G.: Rigid part decomposition in a graph pyramid. In: The 14th Iberoamerican Congress on Pattern Recognition, pp. 758–765. Springer, Heidelberg (2009)
Artner, N.M., Ion, A., Kropatsch, W.G.: Multi-scale 2d tracking of articulated objects using hierarchical spring systems. Pattern Recognition 44(4), 800–810 (2010)
Birchfeld, S.: Klt: An implementation of the kanade-lucas-tomasi feature tracker (March 2008), http://www.ces.clemson.edu/~stb/klt/
Celasun, I., Tekalp, A.M., Gokcetekin, M.H., Harmanci, D.M.: 2-d mesh-based video object segmentation and tracking with occlusion resolution. Signal Processing: Image Communication 16(10), 949–962 (2001)
Chen, H.T., Liu, T.L., Fuh, C.S.: Segmenting highly articulated video objects with weak-prior random forests. In: European Conference on Computer Vision, pp. 373–385. Springer, Graz (2006)
Drouin, S., Hébert, P., Parizeau, M.: Incremental discovery of object parts in video sequences. Computer Vision and Image Understanding 110, 60–74 (2008)
Gavrila, D.M.: The visual analysis of human movement: A survey. Computer Vision and Image Understanding 73(1), 82–980 (1999)
Godec, M., Leistner, C., Saffari, A., Bischof, H.: On-line random naive bayes for tracking. In: ICPR, pp. 3545–3548 (2010)
Kropatsch, W.G., Haxhimusa, Y., Pizlo, Z., Langs, G.: Vision pyramids that do not grow too high. Pattern Recognition Letters 26(3), 319–337 (2005)
Lauer, F., Schnrr, C.: Spectral clustering of linear subspaces for motion segmentation. In: ICCV, pp. 678–685. IEEE, Los Alamitos (2010)
Li, H., Lin, W., Tye, B., Ong, E., Ko, C.: Object segmentation with affine motion similarity measure. In: Multimedia and Expo., pp. 841–844 (2001)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 104(2-3), 90–126 (2006)
Nordberg, K., Zografos, V.: Multibody motion segmentation using the geometry of 6 points in 2d images. In: ICPR, pp. 1783–1787. IEEE, Istanbul (2010)
Ross, D.A., Tarlow, D., Zemel, R.S.: Learning articulated structure and motion. International Journal of Computer Vision 88(2), 214–237 (2010)
Tuceryan, M., Chorzempa, T.: Relative sensitivity of a family of closest-point graphs in computer vision applications. Pattern Recognition 24(5), 361–373 (1991)
Walther, T., Würtz, R.P.: Unsupervised learning of human body parts from video footage. In: 2nd Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, pp. 336–343 (2009)
Yan, J., Pollefeys, M.: A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video. IEEE Trans. Pattern Anal. Mach. Intell. 30(5), 865–877 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Artner, N.M., Ion, A., Kropatsch, W.G. (2011). Spatio-Temporal Extraction of Articulated Models in a Graph Pyramid. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-20844-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20843-0
Online ISBN: 978-3-642-20844-7
eBook Packages: Computer ScienceComputer Science (R0)