2024-03-29T00:33:45Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/127652016-02-16T04:40:15Zcom_10261_106com_10261_4col_10261_359
Automatic learning of 3D pose variability in walking performances for gait analysis
Rius, Ignasi
Gonzàlez, Jordi
Mozerov, Mikhail
Roca, F. Xavier
Computer vision
Human motion modelling
Gair analysis and recognition
Dynamic programming
11 pages, 5 figures, 3 tables.
This paper proposes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. First, a Dynamic Programing synchronization algorithm is presented in order to establish a mapping between postures from different walking cycles, so the whole training set can be synchronized to a common time pattern. Then, the model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally statistics about the observed variability of the postures and motion direction are also computed at each time step. As a result, in this work we have extended a similar action model successfully used for tracking, by providing facilities for gait analysis and gait recognition applications.
This work has been supported by EC grants IST-027110 for the HERMES project and IST-045547 for the VIDI-Video project, and by Spanish MEC under projects TIN2006-14606 and DPI-2004-5414. Jordi Gonzàlez also acknowledges the support of a Juan de la Cierva Postdoctoral fellowship from the Spanish MEC. The database used in this project was obtained from mocap.cs.cmu.edu which was created with funding from NSF EIA-0196217.
http://hdl.handle.net/2117/2702
Peer reviewed
2009-05-04T13:09:46Z
2009-05-04T13:09:46Z
2008-01
artículo
http://purl.org/coar/resource_type/c_6501
International Journal for Computational Vision and Biomechanics 1(1): 33-43 (2008)
0973-6778
http://hdl.handle.net/10261/12765
en
Preprint
http://paginas.fe.up.pt/~ijcvb/editions_v1_n1.htm
open
498860 bytes
application/pdf
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