Abstract
Human motion analysis from images is meticulously related to the development of computational techniques capable of automatically identifying, tracking and analyzing relevant structures of the body. This work explores the identification of such structures in images, which is the first step of any computational system designed to analyze human motion. A widely used database (CASIA Gait Database) was used to build a Point Distribution Model (PDM) of the structure of the human body. The training dataset was composed of 14 subjects walking in four directions, and each shape was represented by a set of 113 labelled landmark points. These points were composed of 100 contour points automatically extracted from the silhouette combined with an additional 13 anatomical points from elbows, knees and feet manually annotated. The PDM was later used in the construction of an Active Shape Model, which combines the shape model with gray level profiles, in order to segment the modelled human body in new images. The experiments with this segmentation technique revealed very encouraging results as it was able to gather the necessary data of subjects walking in different directions using just one segmentation model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ogawara K, Li X, Ikeuchi K. Marker-less Human Motion Estimation using Articulated Deformable Model. IEEE International Conference on Robotics and Automation, 2007. p. 46–51.
Wei XK, Chai J. Intuitive Interactive Human-Character Posing with Millions of Example Poses. IEEE Computer Graphics Applications, 2011. 31(4): p. 78–88.
Al-Huseiny M, Mahmoodi S, Nixon M. Gait Sequence Synthesis and Reconstruction. IEEE Transactions on PAMI 2008. 30.8: p. 1385–1399.
Das Choudhury S, Tjahjadi T. Gait recognition based on shape and motion analysis of silhouette contours. Computer Vision and Image Understanding, 2013. 117(12): 1770–85.
Tran C, Trivedi MM. Human body modelling and tracking using volumetric representation: Selected recent studies and possibilities for extensions. Second ACM/IEEE International Conference on Distributed Smart Cameras, 2008. p. 1–9.
Cootes TF, Taylor CJ, Cooper DH, Graham J. Active Shape Models-Their Training and Application. Computer Vision and Image Understanding, 1995. 61(1): p. 38–59.
Cootes TF, Taylor CJ, Cooper DH, Graham J. Training Models of Shape from Sets of Examples. BMVC92. Springer London, 1992. p. 9–18.
Jang C, Jung K. Human pose estimation using Active Shape Models. Proceedings of World Academy of Science: Engineering & Technology 46 (2008).
Pourjam E, Ide I, Deguchi D, Murase H. Segmentation of Human Instances Using Grab-cut and Active Shape Model Feedback. Proceedings of IAPR Conference on Machine Vision Applications (MVA) 2013. p. 77–80.
Hofmann M, Geiger J, Bachmann S, Schuller B, Rigoll G. The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits. Journal of Visual Communication and Image Representation 2014. 25(1): p. 195–206.
Nixon MS, Tan T, Chellappa R. Human Identification Based on Gait. Vol 4, Springer; 2010.
Yu S, Tan D, Tan T. A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition. 18th International Conference on Pattern Recognition, 2006. p. 441–444.
Cootes TF, Taylor CJ, Lanitis A. Multi-resolution search with active shape models. Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol.1 p. 610–612.
Hamarneh G. Active Shape Model Software [Internet]. 1999. Available from: http://www.cs.sfu.ca/~hamarned/software/code/asm.zip Last accessed June 2012.
Acknowledgments
The first author would like to thank the support of the Ph.D. grant with references SFRH/BD/28817/2006 from Fundação para a Ciência e Tecnologia (FCT), in Portugal. This work was partially developed under the scope of the project with reference PTDC/BBB-BMD/3088/2012 financially supported by FCT. The research described in this chapter use CASIA Gait Database collected by Institute of Automation, Chinese Academy of Sciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vasconcelos, M.J.M., Tavares, J.M.R.S. (2015). Human Motion Segmentation Using Active Shape Models. In: Tavares, J., Natal Jorge, R. (eds) Computational and Experimental Biomedical Sciences: Methods and Applications. Lecture Notes in Computational Vision and Biomechanics, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-15799-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-15799-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15798-6
Online ISBN: 978-3-319-15799-3
eBook Packages: EngineeringEngineering (R0)