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Title
CMRT09
Author
Stilla, Uwe

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3A/V4 — Paris, France, 3-4 September, 2009
39
the samples S'” eS r 1 available for each C y , the classification of an unlabeled sample
x can be performed by finding the category with the closest first
principal geodesics to x. The corresponding motion status of a
vehicle is found by
/ =argmin||log(//' l v(11 x)||, ye {1,2} (6)
Generally, it is claimed that the classification of vehicle status
can successfully run based solely on the first principal
geodesics of a movement category. Although there are
significant variations in shape over one category, the first
principal geodesics H is assumed to summarize the essential
shape features of vehicle point sets in terms of only
distinguishing between binary motion statuses.
3.3 Results
We used the same vehicle datasets as derived in the section 2 to
assess the proposed algorithm intended for classifying the
motion status. Both of datasets are acquired over
300x400 m 2 dense urban areas with averaged point density of
about 1.4 pts/ m~ . The only one difference between them is that
the first one used is co-registered from multiple strips rather
than one-path. The classification results of vehicle motion status
are presented in Fig.5. To access the performance of Lie group
based classifier, minimum distance classifier was used to
classify the same datasets based on the feature space spanned
by vehicle parametrization.
The test dataset each consists of more than 50 vehicles
successfully detected by vehicle extraction process. A set of 5
vehicle samples from each motion category is manually
selected to train the classifier for vehicle motion status at first.
It can be expected that poorly chosen training samples due to
the strong shape variability in the category of moving vehicle
could have a negative effect on classification performance.
Therefore, the selection of training data for moving vehicle
category should be carried out in such way that the fundamental
shape information are expressed and generalized. Receiver
Operating Characteristic (ROC) curves are generated by
comparing classification results with reference data manually
acquired by human interpretation and shown in Fig.6 for
respective test datasets.
'atf
~ w
(b)
Figure 5. Vehicles motion classification results for dataset 1 and
II (top-view of vehicle point sets). Blue: moving; Red:
stationary; Yellow: uncertain.
(a) (b)
Figure 6. ROC curves for vehicle motion classification, (a)
Dataset I; (b) Dataset II.
3.4 Discussion
Since we do not have real “ground truth” for vehicle motion
which could be simultaneously captured along the scanning
campaigns by an imaging sensor as described in Toth and
Grejner-Brzezinska, (2006), the results are firstly assessed with
respect to human examination abilities. Based on the context
relations the vehicle movement could be roughly distinguished
between moving vehicles and stationary ones. Note that the
along-track motion cannot be resolved on principle if the true
length is unknown, our evaluation are inherently biased by
ambiguities introduced by the incorrect vehicle length.
It can be found out from the results displayed above that most
of detected moving vehicles appear in the heavily travelled
roads such as flyovers and main streets of city and the vehicles
classified as motionless are mostly found in the parking lots or
along road margins. The yellow class indicates the vehicles of
uncertain status which are all nearly placed very close to each
other in a parking lot and are excluded from the motion
classification step due to the shape irregularity. False alarms
from motion classification by our approach usually appear for
slowly moving vehicles which travelled not perpendicular to the
flight direction or those moving ones that are shaped by
anomaly sample points in ALS data due to vegetation occlusion
or unstable reflection properties. As indicated in ROC curves,
the Lie group based classifier outperforms the minimum
distance classifier in both cases, as its ability to generalize
various shapes from training data, even for worst-cases, is
demonstrated. It can also be observed that the second test