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Ta b l e 2
Ego-motion tracking statistics for different noise levels when sample rates are de-
creased (notation is same as Table 1).
Noise
t
x
t
z
θ
z
Variance
m
s
σ
s
m
g
σ
g
m
s
σ
s
m
g
σ
g
m
s
σ
s
m
g
σ
g
0
4.02 4.11 0.04 0.05
0.06 0.07 0.06 0.06
0.52 0.71 0.26 0.47
2
4.26 4.31 0.03 0.03
0.03 0.03 0.03 0.03
0.71 0.83 0.37 0.47
4
6.74 6.87 0.02 0.03
1.72 1.75 0.08 0.09
0.82 0.94 0.51 0.72
6
4.95 5.03 0.22 0.23
0.16 0.18 0.17 0.17
0.73 0.79 0.61 0.73
8
4.60 4.62 0.28 0.34
0.11 0.13 0.09 0.10
0.58 0.72 0.38 0.43
12
10.78 12.81 0.35 0.41 10.84 15.19 0.33 0.38 0.78 0.94 0.58 0.67
30
30
20
20
10
10
0
0
−10
−10
−20
−20
50
55
60
65
70
75
50
55
60
65
70
75
Frame number
Frame number
(a) Without additive noise
(b) With noise
Fig. 7
Combination of translation and rotation (
t
z
). Comparison of the ground-truthed motion
and estimation using the gait-based and the STK-based approaches with random noise added:
(a) no additive noise, (b) (0.0,12.0) noise. “-" - ground truth, “o" - STK-based estimates, and
“
×
" - gait-based estimates.
parameters at sampling rates of up to every sixth frame on this sequence, but it was
not possible to make a comparison because the basic tracker was unable to recover
the motion parameters.
5.3
Synthetic Human Motion Sequences
In the next set of experiments, we extended the evaluation to a full 3-D environ-
mental “walk-through", using a computer animation package to provide synthesis of
translational and rolling gait. This environment is far more realistic than the checked
target, but we can still compare the recovered parameters against ground truth as the
gait parameters can be fully programmed using the “Quake" engine, which is pub-
licly available at http://www.codeproject.com/managedcpp/quake2.asp. We used a
demonstration video (“indoors navigation") in which the “observer" wanders in a
castle in changing illumination, which together with the more complex 3-D environ-
ment can cause more missed feature correspondences than with the checked target.
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