CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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tions) are observed and evaluated. A common aim is to de 
scribe the observed data and to detect atypical or threatening 
events. 
Other areas of situation analysis besides driver assistance 
(Reichardt 1995) may include traffic situation representation, 
surveillance applications (Beynon et al. 2003), sport video 
analysis or even customer tracking for marketing analysis 
(Leykin et al. 2005). 
(Kumar et al. 2005) developed a rule-based framework for 
behavior and activity detection in traffic videos obtained 
from stationary video cameras. For behavior recognition, 
interactions between two or more mobile targets as well as 
between targets and stationary objects in the environment 
have been considered. The approach is based on sets of pre 
defined behavior scenarios, which need to be analyzed in 
different contexts. 
(Yung et al. 2001) demonstrate a novel method for automatic 
red light runner detection. It extracts the state of the traffic 
lights and vehicle motions from video recordings. 
1.3 Image and Trajectory Processing 
The cameras deployed cover overlaid or adjacent observation 
areas. With it, the same road user can be observed using dif 
ferent cameras from different view positions and angles. The 
traffic objects in the image data can be detected using image 
processing methods. 
The image coordinates of these objects are converted to a 
common world coordinate system in order to enable the 
tracking and fusion of the detected objects of the respective 
observation area. High precision in coordinate transformation 
of the image into the object space is required to avoid mis- 
identification of the same objects that were derived from 
different camera positions. Therefore, an exact calibration 
(interior orientation) as well as knowledge of the position and 
view direction (exterior orientation) of the camera is neces 
sary. 
Since the camera positions are given in absolute geographical 
coordinates, the detected objects are also provided in world 
coordinates. 
The approach is subdivided into the following steps. Firstly, 
all moving objects have to be extracted from each frame of 
the video sequences. Secondly, these traffic objects have to 
be projected onto a geo-referenced world plane. Afterwards, 
these objects are tracked and associated to trajectories. One 
can now utilize the derived information to assess comprehen 
sive traffic parameters and to characterize trajectories of 
individual traffic participants. 
1.4 Scenario 
The scenario has been tested at the intersection Rudower 
Chaussee / Wegedomstrasse, Berlin (Germany) by camera 
observation using three cameras mounted at a comer building 
at approximately 18 meters height. The observed area has an 
extent of about 100x100 m and contains a T-section. Figure 1 
shows example trajectories derived from images taken from 
three different positions. The background image is an ortho 
photo, derived from airborne images. 
Figure 1. Orthophoto with example trajectories 
The aim is the description of the trajectories by functions 
with a limited number of parameters. Source destination 
matrices could be determined at these crossroads through 
such parameters without any further effort. A classification 
approach shall be used here. 
2. PROCESSING APPROACH 
2.1 Video Acquisition and Object Detection 
In order to receive reliable and reproducible results, only 
compact digital industrial cameras with standard interfaces 
and protocols (e.g. IEEE 1394, Ethernet) are deployed. 
Different image processing libraries or programs (e.g. 
OpenCV or HALCON) are available to extract moving ob 
jects from an image sequence. We used a special algorithm 
for background estimation, which adapts to the variable 
background and extracts the desired objects. The dedicated 
image coordinates as well as additional parameters like size 
and area were computed for each extracted traffic object. 
2.2 Sensor Orientation 
The existing tracking concept is based on extracted objects, 
which are geo-referenced to a world coordinate system. This 
concept allows the integration or fusion of additional data 
sources. The transformation between image and world coor 
dinates is based on collinearity equations. The Z-component 
in world coordinates is deduced by appointing a dedicated 
ground plane. An alternative is the use of a height profile. 
Additionally needed input parameters are the interior and 
exterior orientation of the camera. For the interior orientation 
(principal point, focal length and additional camera distor 
tion) of the cameras the 10 parameter Brown distortion 
model (Brown 1971) was used. The parameters are being 
determined by a bundle block adjustment. 
Calculating the exterior orientation of a camera (location of 
the projection centre and view direction) in a well known 
world coordinate system is based on previously GPS meas 
ured ground control points (GCPs). The accuracy of the 
points is better than 5 cm in position and hight. The orienta 
tion is deduced through these coordinates using DLT and the 
spatial resection algorithm (Luhmann 2006).