CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
accomplish this task, we proposed a hybrid strategy that 
integrates context-guided progressive method with 3-d 
segmentation based classification. Experiments demonstrated 
that the assimilation of these two approaches (Fig. 1) can make 
our vehicle extraction from LiDAR data of urban areas more 
competent and robust, even against complex scenes. 
Raw LiDAR data 
Figure 1. Integrated scheme for vehicle extraction. 
2.1 Context-guided extraction 
This extraction strategy comprises knowledge about how and 
when certain parts of the vehicle and context model of traffic 
related objects in urban areas are optimally exploited, thereby 
forming the basic control mechanism of the extraction process. 
In contrast to other common approaches dealing with LiDAR 
data analysis, it neither uses the reflected intensity for 
extraction nor combines multiple data sources acquired 
simultaneously. The philosophy is to exploit geometric 
information of ALS data as much as possible primarily based 
on such context-relation that vehicles are generally placed upon 
the ground surface. Moreover, the approach on the one side can 
be viewed as a processing strategy progressively reducing 
“region of interest”. It is subdivided into four steps: ground 
level separation, geo-tiling and filling, vehicle-top detection and 
selection, segmentation, which are respectively elaborated in 
detail in Yao et al., (2008)a. An exemplary result on one co 
registered dataset is shown in Fig.2. 
Figure 2. Vehicle extraction result as white outlined contours 
for test data I using context-guilded method. 
2.2 3D segmentation based classification 
Since many vehicles in modem cities might travel on the 
elevated roads such as flyover or bridge, the context relation 
abided by the method in section 2.1 does not always hold. 
Therefore, we introduced a 3D object-based classification 
strategy for extracting semantic objects directly from LiDAR 
point cloud of urban areas. It could either extract two object 
classes - vehicle and elevated road simultaneously or only 
elevated road, where vehicle can further be detected considering 
elevated road here as ground. The ALS data is firstly subjected 
to the segmentation process using nonparametric clustering tool 
- mean shift (MS). The obtained results are usually not able to 
give a significative description of distinct natural and man-made 
objects in complex scenes, even though MS does a genuine 
clustering directly on 3D point cloud to discover various 
geometric modes in it. Hence, the initial resulted point segments 
have to be handled under the global optimization criterions to 
generate more consistent subsets of laser data. For it, a modified 
normalized-cuts (Ncuts) is applied with the sense of perceptual 
grouping. Finally, based on derived features of spatially 
separated point clusters that potentially correspond to semantic 
object entities, classification is performed to evaluate them to 
extract the flyover and vehicle (Yao et al., 2009). Applying this 
approach to a one-path dataset yielded Fig.3. 
Figure 3. Vehicle (green) and flyover extraction results for test 
data II using 3D segentation based classification. 
3. VEHICLE MOTION INDICATION 
For extracted vehicles resulted from last step, the parameterized 
model for point sets of single vehicles can then be produced by 
shape analysis. From the parameterized features of vehicle 
shape, the across-track vehicle motion (-component) is able to 
be indicated unambiguously based on the moving vehicle model 
in ALS data, whereas the along track motion cannot be implied 
without prior knowledge about individual vehicle sizes. In this 
section, the vehicle motion status is attempted to be inferred up 
to the across-track direction w ithout derive the velocity. 
3.1 Vehicle Parametrization 
Generally, the laser data provide us a straightforward 3D 
parameterization, as vehicle forms change more vertically than 
horizontally. To refine the 3D vehicle envelope model (Yao et 
al., 2008b), however, is difficult, because the laser point density 
acquired under common configurations is usually not sufficient 
to model the vertical profile of a vehicle. The situation is even 
more degraded by motion artifacts, because the large relative 
velocity of the sensor to object results in fewer laser points, 
making vehicle appears like a blob. Consequently, it is not easy 
to analytically model the vertical vehicle profiles from ALS 
data, which would be a simple task for much denser terrestrial 
laser data. 
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