Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Motion Tracking by Registering an Articulated Surface to 3-D
  Points and Normals   We address the problem of human motion tracking by registering a surface to
3-D data. We propose a method that iteratively computes two things: Maximum
likelihood estimates for both the kinematic and free-motion parameters of a
kinematic human-body representation, as well as probabilities that the data are
assigned either to a body part, or to an outlier cluster. We introduce a new
metric between observed points and normals on one side, and a parameterized
surface on the other side, the latter being defined as a blending over a set of
ellipsoids. We claim that this metric is well suited when one deals with either
visual-hull or visual-shape observations. We illustrate the method by tracking
human motions using sparse visual-shape data (3-D surface points and normals)
gathered from imperfect silhouettes.

26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat