![](bg7.png)
Table 3: Classification Results for Adult Male Sequence
Starting at Epoch with Extended Wrong Classification.
Method Correct Classification
Raw Data .6291
Discounted Dempster-Shafer .7549
Kalman (High Gain) .6725
Kalman (Medium Gain) .6920
Kalman (Low Gain) .7961
Kalman (Ultra-low Gain) .8568
In summary, the proposed Kalman filter-based
temporal evidence accumulation algorithm
outperformed the traditional Dempster-Shafer
algorithm on all three of the datasets in this real-
world application from an automotive airbag
suppression system.
7 CONCLUSIONS AND FUTURE
WORK
We have introduced the notion that when integrating
evidence from a temporal stream of sensor inputs, an
approach based on estimation theory and human
reasoning provides superior performance to a
traditional evidential reasoning approach based on
Dempster-Shafer. We posited that this is due to the
fact that the Dempster-Shafer approach is based on
the concept of evidential independence which
mandates the data be derived from different sensors
(as originally envisioned by Dempster) and that for a
single sensor a weaker statistical independence is all
that can be assured.
We reviewed various approaches for evidence
accumulation. We then developed an alternative
Kalman filter representation from first principles and
identified the key uncertainty terms as being: the
estimate uncertainty:
k
E
2
ˆ
, the measurement
uncertainty:
2
M
, and the system uncertainty:
k
h
2
.
We proposed that the concept of conflict in the
incoming evidential states can be used as a means of
estimating the system uncertainty. The approach was
tested on a real-world automotive airbag suppression
application which consisted of a high resolution
camera providing real-time classification inputs to
our evidence accumulation system. An ultra-low
gain Kalman filter out-performed the traditional
Dempster-Shafer algorithm, which parallels the
findings from human cognition where long term
accumulation of evidence is best considered an
estimation technique and recent evidence is highly
discounted in favour of the historical accumulation
of evidence.
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