The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B3b. Beijing 2008 
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skewing (a v ) of the vehicle shape. Therefore, for the most 
cases the vehicle will appear as deformed parallelogram in the 
scanning data - combination of both motion effects. In principle 
the shape deformation of the vehicle can be used to 
quantitatively derive the motion status, but a prerequisite must 
be fulfilled that the true vehicle length is known and its 
accuracy and sensitivity depend strongly on various impact 
factors, such as point density, horizontal position error, or 
physical properties of surface. In practice it is firstly not easy to 
access the performance of this approach; a great amount of test 
data is required to prove the feasibility and robustness. 
4. GENERAL APPROACHES AND ANALYSIS 
In the last two decades general approaches of 3D object 
representation and recognition have been widely investigated in 
the computer science community (Arman & Aggarwal, 1993; 
Besl & Jain, 1986). Being different from the classical object 
recognition, different methods, such as graph-based shape 
matching/fitting and point pattern matching were developed to 
directly conduct recognition process in the 3D range data. 
Formerly, the most developed methods used to deal with the 
small-scale dataset of reverse engineering. Due to the high level 
of detail the smoothness, or curvature-based segmentation 
algorithms are adopted to facilitate the recognition process; 
ALS data over urban areas characterize the large coverage, very 
complex scene and low LoD concerning the shape fidelity. 
Rather than generic, application-specific methods have been 
widely employed for object extraction, e.g. for building, road 
and tree. The almost unique standard operation to ALS data is 
filtering of non-ground points, motivated by topographical 
mapping applications, to obtain the DEM. 
Context object 
Ç Fly-over^) Ç Road fopen area or yard) C Building Tree J 
i ¡¡es 1.5- 3.5m as sampled ; 
i leaves gaps , 
points above 
• ..> 
is dose to 
casts shadow i hangs over 
on and occludes 
Vehicle object 
f Single N Vehicle 
Vehicle J V queue J 
Relations 
Figure 4. Context-relation model 
A progressive processing strategy is proposed here to tackle the 
problem of traffic monitoring using ALS data: by exploring 
context relations. It is a direct processing chain being in 
accordance with human inference. The structure of this 
algorithm is organized based on the context-relation model 
(Fig.4) retaining the knowledge of various object relations in 
the urban area, where the dotted arrow indicates relation 
direction. It is executed in a progressive and hierarchical way 
controlled by our strategy for vehicle detection (Fig.5). 
The algorithm starts with raw 3D point cloud of urban area. 
Some basic operations for preprocessing laser data could be run 
beforehand, like outline remove and hole filling. Then, a rough 
separation of ground points and non-ground points is to be 
carried out, using the height histogram thresholding or building 
UDAR raw data 
T 
Ground point 
separation 
Ground points/non 
ground points 
4 
Æ 
W 
DGM filtering 
DGM 
A 
Filtering 
vehicle points 
r 
w 
Building labeling 
* + 
Vehicle point 
candidates Building outline 
Elimination of ^ 
non-vehicle points 
Vehicle points 
(VHM) 
* 
Segmentation 
+ 
Vehicle objects 
Am 
Road layer 
> 
GIS Map 
Vegetation region., 
Æ 
V* 
mr 
Coarse vegetation 
region delineation 
.Data gap left by vehicle 
above 
Figure 5. Flow chart of method 
labeling algorithm; the objective of this step is to mask out non 
ground points or man-made objects like buildings where the 
vehicle is assumed not to appear. The ground points are viewed 
to build ground level surface in the urban which consist of not 
only road but also courtyard. A smooth and continuous surface 
could be imagined to be generated from the ground points as the 
reference surface for ground level, being like the terrain surface 
(DTM) after filtering, which can be represented in the form of 
point cloud, surface meshing or analytical function. A height 
interval of 0.5 to 2.0m over this ground surface is set to slice a 
laser data layer 5j including all laser points p k regarded as 
vehicle hypotheses (Formula.3). Afterwards, the vehicle 
candidate points are delivered to the process eliminating 
disturbing objects like tree points, wire pole, parterre or some 
anomaly points. In order to distinguish between different 
confused objects, various extern information sources such as 
GIS/map can be used to mark building and road regions. 
Vegetation regions can also be first delineated, beneath which 
potential vehicles are to be searched and validated with special 
efforts. The remaining laser points are transformed to vehicle 
height model (VHM) in regular grid - normalized DSM for 
vehicle, based on which single vehicle extraction and modeling 
is to be carried out. Laser point gaps (holes) on the ground 
surface left by impervious vehicles provide us another clue to 
the presence of vehicle. All laser points belonging to or lying 
within certain buffer interval (± 0.05m) of ground surface are 
projected into a 2D plane regularly gridded, where cell size is 
selected according to the laser point density. Each cell is 
assigned with a value indicating either whether there are points 
fall into the grid representing a small neighborhood, so the gap 
will be retained as small dark area in this image. Some 
experimental results are illustrated in Fig.6 
Si = {p k e S : z k - z Sr(Xk n) < Ah^} 
(3)