The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008 
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Therefore a laser point cloud of high density usually demands a 
very small footprint to accomplish the surveying task. As we 
have mentioned above, the vegetation occlusion is a key factor 
for vehicle detection in laser data of urban areas, the penetration 
ability of the laser sensor against vegetation has to be examined 
by registering multi-retum-pulses in one echo signal. 
Meanwhile, another type of commercial ALS systems, named 
full-waveform LiDAR, has been developed recently (Jutzi & 
Stilla, 2006; Wagner et al., 2007). The entire analogue echo 
waveform, i.e. the time-dependent variation of received signal 
power, for each emitted laser pulse is digitized and recorded. 
This new sensor technique was recently employed to analyze 
and estimate the biological volume of vegetations, whose 
internal structures are partially penetrated by the laser beam and 
can be reconstructed. It can be inferred that penetration rate of 
single laser pulse is proportional to the footprint size. Thus in 
case of vehicle detection, a certain diameter of laser footprint is 
necessary to enable the emitted laser pulse to penetrate the 
vegetation and hit the potential interest objects beneath it. 
Considering other demands mentioned above, a compromise 
between footprint size and point density should be made in the 
mission plan to achieve the optimal configuration of ALS data 
acquisition for traffic analysis. Another two extra products 
derived by waveform decomposition - pulse width and intensity, 
which describe physical reflection properties of the illuminated 
surface other than geometric information, could also provide us 
useful clues to the existence of vehicles. 
2.3 Field of view. The FOV of laser scanning, namely swath 
width, is the extent of data coverage perpendicular to the fight 
path. It depends on the flight height and scan angle which refer 
to the application - specific parameters and can be selected in 
view of project objectives to optimize the system performance. 
For traffic monitoring applications we assume that the FOV can 
be chosen without special restriction. 
2.4 Scan pattern. Scan pattern of laser data acquisition is 
generated by deflecting the laser beam using an oscillating or a 
multi-faceted rotating mirror. Parallel line and z-shaped are two 
most common scan patterns used by current commercial 
systems. The point distribution on the ground of z-shaped can 
be less homogeneous in the along-track direction than parallel 
line. The orientation of the flight path with respect to the main 
street of test site plays a role in the quality of acquired laser 
points used for object recognition, especially for vehicle queues. 
When a vehicle queue spreads parallel to the flight path, the 
point distribution of single vehicles is homogeneous among the 
vehicle queue model; the illuminated surface model depends 
uniformly on the scan angle. When a vehicle queue spreads 
perpendicular to the flight path, the point distribution of single 
vehicles is not homogeneous any longer due to the varied 
incidence angle; the illuminated surface model of each single 
vehicle depends on its relative position to the nadir. It seems 
that both modes of data recording can not prevail over each 
other towards traffic analysis. It has to be further verified by 
quantitative analysis. 
2.4 Minimum detectable object/energy. The minimum 
detectable object/energy within the laser footprint does not 
depend on the object size, but primarily on its reflectivity, when 
ignoring other factors that influence detectability. This 
expression has signified that the comprehensive knowledge of 
analysis and modeling of material properties of vehicle surfaces 
play a key role in case of traffic objects acquisition and 
recognition from ALS. The effect of occlusions due to 
vegetation because of the minimum detectable object/energy is 
also needed to be studied exactly. 
a b 
c d 
Figure 1. Characteristics of forward-looking laser data. a,b) 
pulse dispersed by vehicles; c,d) shadow cast by buildings 
3. VEHICLE MODEL 
Research works on vehicle detection using the imaging sensors, 
such as optical camera, IR camera or SAR, usually are 
distinguished based on the underlying type of modeling. There 
are generally two types of vehicle models - appearance-based 
implicit model and explicit model in 2D or 3D represented by a 
filter or wire-frame (Hinz, 2004). Some authors have also 
modeled queue as global feature for vehicles and made use of it 
and local vehicle features in a synergetic fashion for vehicle 
detection from various kinds of remote sensing platforms. For 
the purpose of better understanding of the sensor mechanism 
und data characteristics, it is assumed that the vehicle modeling 
is equally required for vehicle detection in the context of traffic 
monitoring from ALS systems, although the consistent object 
modeling in the laser data seems to be very difficult. 
Here the stationary vehicle model refers to the parking vehicles 
and temporarily motionless ones (mainly cars in urban areas), 
which comprises an important category of vehicle status for 
deriving traffic parameters. 
The typical object model usually compiles knowledge about 
geometric, radiometric, and topological characteristics. The 
geometric property is considered to be the essential part of the 
vehicle model (Fig.2), which is used to support the recognition 
task in the laser data. The intensity of received laser pulses is so 
far hardly utilized due to lack of the calibration and the insight 
into physical background. The model represents the standard 
case, i.e. the appearance of vehicles is not affected by relations 
to other objects, e.g. shadow cast by buildings and vegetation 
occlusion. Moreover, since the detection of vehicles beneath the 
vegetation is, from our viewpoint, also very important, a new 
3.1 Stationary vehicle