TY - GEN
T1 - Scale-independent spatio-temporal statistical shape representations for 3D human action recognition
AU - Körner, Marco
AU - Haase, Daniel
AU - Denzler, Joachim
PY - 2012
Y1 - 2012
N2 - Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. We propose a scheme for representing human actions in 3d, which is designed to be invariant with respect to the actor's scale, rotation, and translation. Our approach employs Principal Component Analysis (PCA) as an exemplary technique from the domain of manifold learning. To distinguish actions regarding their execution speed, we include temporal information into our modeling scheme. Experiments performed on the CMU Motion Capture dataset shows promising recognition rates as well as its robustness with respect to noise and incorrect detection of landmarks.
AB - Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. We propose a scheme for representing human actions in 3d, which is designed to be invariant with respect to the actor's scale, rotation, and translation. Our approach employs Principal Component Analysis (PCA) as an exemplary technique from the domain of manifold learning. To distinguish actions regarding their execution speed, we include temporal information into our modeling scheme. Experiments performed on the CMU Motion Capture dataset shows promising recognition rates as well as its robustness with respect to noise and incorrect detection of landmarks.
KW - Human action recognition
KW - Manifold learning
KW - PCA
KW - Shape model
UR - http://www.scopus.com/inward/record.url?scp=84862165178&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84862165178
SN - 9789898425980
T3 - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
SP - 288
EP - 294
BT - ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods
T2 - 1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012
Y2 - 6 February 2012 through 8 February 2012
ER -