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Fig. 7.3
Determination of
the ground truth
ple, we observed that for a walking person moving the arms backwards the object
hypothesis does not include the arms due to their velocity.
The experimental evaluation shows that the proposed method is a promising tech-
nique for applications in the field of human-robot interaction in industrial produc-
tion scenarios. It does not rely on colour cues, and the objects in the scene are
detected and tracked based on a weak model (a cylinder) which does not require
any a priori knowledge about the objects.
7.3 Detection and Spatio-Temporal Pose Estimation of Human
Body Parts
In this section, which is adopted from Barrois and Wöhler (
2008
), their method
for model-based object detection and spatio-temporal three-dimensional pose esti-
mation (cf. Sect.
2.2.1.2
) is evaluated by analysing three realistic image sequences
displaying a hand-forearm limb moving at non-uniform speed in front of a complex
cluttered background (cf. Figs.
2.17
and
7.4
). The distance of the hand-forearm
limb to the camera amounts to 0
.
85-1
.
75 m, the image resolution to 2-3 mm per
pixel. For acquisition of stereo image pairs a Digiclops CCD camera system is used.
The time step between subsequent image pairs amounts to
t
50 ms. Spacetime
stereo information according to Sect.
1.5.2.5
is determined based on triples of sub-
sequent stereo image pairs, where we have set
δ
max
=
=
2. Ground truth information
has been determined based on three markers attached to points located on the up-
per forearm (
W
p
1
), the wrist (
W
p
2
), and the front of the hand (
W
p
3
) as depicted
in Fig.
7.3
. The three-dimensional coordinates of the markers were determined by
bundle adjustment.
For each of the two parts of the hand-forearm model, the corresponding 5 trans-
lational and rotational pose parameters (denoted by the vector
T
) are determined
independently. For the evaluation, our method is employed as a 'tracking by detec-
tion' system; i.e. the pose
T
(t)
and the pose derivative
T
(t)
are used to compute a
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