© The Institution of Engineering and Technology
This study presents a new approach to classify human body poses by using angular constraints and variations of body joints. Although different classifications of the poses have been previously made, the proposed approach attempts to create a more comprehensive, accurate and extensible classification by integrating all possible poses based on angles of movement in human joints. The angular variations in all body joints can determine any possible poses. The joint angles from the body axis are computed in the three-dimensional space. In order to train and classify the pose in an automated manner, support vector machines (SVMs) were used. Experiments were carried out on both benchmark (CMU dataset) and in-house simulated (POSER dataset) poses to evaluate the performance of the proposed classification scheme.
References
-
-
1)
-
G. Mori ,
J. Malik
.
Recovering 3D human body configurations using shape contexts.
IEEE Trans. Pattern Anal. Mach. Intell.
,
7 ,
1052 -
1062
-
2)
-
J. Arie ,
Z. Wang ,
P. Pandit ,
S. Rajaram
.
Human activity recognition using multi dimensional indexing.
IEEE Trans. Pattern Anal. Mach. Intell.
,
9 ,
1091 -
2005
-
3)
-
Kumar, P., Sengupta, K., Ranganath, S.: `Real time detection and recognition of human profiles using inexpensive desktop cameras', Proc. Int. Conf. Pattern Recognition (ICPR), 2000, p. 1096–1099.
-
4)
-
Ude, A., Man, C., Riley, M., Atkenson, C.: `Automatic generation of kinematics models for the conversion of human motion capture in to humanoid robot motion', Proc. IEEE Conf. Humanoid Robots, 2000, Cambridge, MA.
-
5)
-
E. Marieb
.
(2002)
Essentials of human anatomy and physiology.
-
6)
-
S. Belongie ,
J. Malik ,
J. Puzicha
.
Shape matching and object recognition using shape contexts.
IEEE Trans. Pattern Anal. Mach. Intell.
,
4 ,
509 -
522
-
7)
-
A. Pierce ,
R. Pierce
.
Expressive movement: posture and action in daily life, sports and the performing arts.
-
8)
-
M. Lee ,
I. Cohen
.
A model-based approach for estimating human 3D poses in static images.
IEEE Trans. Pattern Anal. Mach. Intell.
,
6 ,
305 -
317
-
9)
-
N. Cristianini ,
J. Shawe-Taylor
.
(2000)
An introduction to support vector machines and other kernel-based learning methods.
-
10)
-
C. Bauby ,
A. Kuo
.
Active control of lateral balance for human walking.
Int. J. Biomech.
,
1433 -
1440
-
11)
-
Mittal, A., Zhao, L., Davis, L.: `Human body pose estimation using silhouette shape analysis', Proc. Advanced Video and Signal Based Surveillance Conf., 2003, p. 263–270.
-
12)
-
N. Campbell ,
J. Reece
.
(2002)
Biology.
-
13)
-
R. Cucchiara ,
C. Grana ,
A. Prati
.
Probabilistic posture classification for human behavior analysis.
IEEE Trans. Syst. Man Cybern.
,
1 ,
42 -
55
-
14)
-
Molina-Tanco, L., Hilton, A.: `Realistic synthesis of novel human movements from a database of motion capture examples', Proc. IEEE Workshop on Human Motion, 2000.
-
15)
-
T. Moeslund ,
E. Granum
.
A survey of computer vision-based human motion capture.
J. Comput. Vis. Image Underst.
,
231 -
238
-
16)
-
K. Grumman ,
G. Shakhnarovich ,
T. Darrell
.
Inferring 3D structures with statistical image based model.
Proc. Int. Conf. Comput. Vis.
,
941 -
647
-
17)
-
http://www.coedu.usf.edu/behavior/bares.htm.
-
18)
-
J. Yang ,
Q. Huang ,
Z. Pheng ,
L. Zhange ,
Y. Shi ,
X. Zhao
.
Capturing and analyzing of human motion for designing humanoid motion.
Proc. IEEE Conf. Inf. Acquis.
,
332 -
338
-
19)
-
A. Elgammal ,
C. Lee
.
Inferring 3D body pose from silhouettes using activity manifold learning.
Proc. Int. Conf. Comput. Vis. Pattern Recognit.
,
681 -
688
-
20)
-
POSER, http://www.e-frontier.com.
-
21)
-
O. Chappelle ,
P. Haffner ,
V. Vapnik
.
Support vector machines for histogram based image classification.
IEEE Trans. Neural Netw.
,
5 ,
1055 -
1064
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2008.0086
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