Advances in Vision-Based Human Body Modeling - 3D Modeling and Animation: Synthesis and Analysis Techniques for the Human Body - page 10

and the new 3D position of each point in the global seamless mesh is computed
as a weighted combination of the related bone motions.
The skinned model definition can also be enriched with inverse kinematics -
related data. Then, bone positions can be determined by specifying only the
position of an end effector, e.g., a 3D point on the skinned model surface. No
specific inverse kinematics solver is imposed, but specific constraints at bone
level are defined, e.g., related to the rotation or translation of a bone in a certain
direction. Also muscles , i.e., NURBS curves with an influence region on the
model skin, are supported. Finally, interpolation techniques, such as simple linear
interpolation or linear interpolation between two quaternions (Preda & Prêteux,
2001), can be exploited for key-value-based animation and animation compres-
sion.
Human Motion Tracking and
Recognition
Tracking and recognition of human motion has become an important research
area in computer vision. Its numerous applications contributed significantly to
this development. Human motion tracking and recognition encompasses chal-
lenging and ill-posed problems, which are usually tackled by making simplifying
assumptions regarding the scene or by imposing constraints on the motion.
Constraints, such as making sure that the contrast between the moving people
and the background should be high and that everything in the scene should be
static except for the target person, are quite often introduced in order to achieve
accurate segmentation. Moreover, assumptions such as the lack of occlusions,
simple motions and known initial position and posture of the person, are usually
imposed on the tracking processes. However, in real-world conditions, human
motion tracking constitutes a complicated problem, considering cluttered back-
grounds, gross illumination variations, occlusions, self-occlusions, different
clothing and multiple moving objects.
The first step towards human tracking is the segmentation of human figures from
the background. This problem is addressed either by exploiting the temporal
relation between consecutive frames, i.e., by means of background subtraction
(Sato & Aggarwal, 2001), optical flow (Okada, Shirai & Miura, 2000) or by
modeling the image statistics of human appearance (Wren et al., 1997). The
output of the segmentation, which could be edges, silhouettes, blobs etc.,
comprises the basis for feature extraction. In tracking, feature correspondence