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Stilla, Uwe

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009
D. Pfeiffer 3 , R. Reulke b *
1 Akeleiweg 46D, 12487 Berlin, Germany - david@dltmail.de
h Humboldt-University of Berlin, Computer Science Department, Computer Vision, Rudower Chaussee, Berlin,
Germany - reulke@infonnatik.hu-berlin.de
Commission III, WG III/4 and III/5
KEY WORDS: Object recognition, Scene Analysis, statistical and deterministic trajectory models
Video image detection systems (VIDS) provide an opportunity to analyse complex traffic scenes that are captured by stationary video
cameras. Our work concentrates on the derivation of traffic relevant parameters from vehicle trajectories. This paper examines dif
ferent procedures for the description of vehicle trajectories using analytical functions. Derived conical sections (circles, ellipses and
hyperboles) as well as straight lines are particularly suitable for this task. Thus, it is possible to describe a suitable trajectory’ by a
maximum of five parameters. A classification algorithm uses these parameters and takes decisions on the turning behaviour of vehi
A model based approach is following. The a-priori knowledge about the scene (here prejudged and verified vehicle trajectories) is
the only required input into this system. One confines himself here to straight lines, circles, ellipses and hyperboles. Other common
functions (such as clothoids) are discussed and the choice of the function is being justified.
1.1 Motivation
Traffic management is based on an exact knowledge of the
traffic situation. Therefore, traffic monitoring at roads and
intersections is an essential prerequisite. Inductive loops and
microwave radar systems are the most common detection and
surveillance systems to measure traffic flow on public roads.
VIDS that operate with real time image processing tech
niques became more attractive during the last 15 years
(Michalopoulos 1991), (Wigan 1992), (Setchell et al. 2001),
(Kastrinaki et al. 2003). Traditional traffic parameters like
presence, vehicle length, speed as well as time gap between
two vehicles and vehicle classification (Wei et al. 1996) can
be determined. In contrast to other sensors, the use of local
cameras makes a two-dimensional observation possible and
thus can determine new traffic parameters like congestion
length, source-destination matrices, blockage or accidents
and therefore support the estimation of travel times. Multi
camera systems extend some limitations of single camera
systems (e.g. occlusions, reliability) and enlarge the observa
tion area (Reulke et al. 2008a).
We proposed a framework that autonomously detects atypi
cal objects, behavior or situations even in crowded and com
plex situations (Reulke et al. 2008b). Extracted object data
and object trajectories from multiple sensors have to be
fused. An abstract situational description of the observed
scene is obtained from the derived trajectories. The first step
in describing a traffic scene is to ascertain the normal situa
tion by statistical means. In addition, semantic interpretation
is also derived from statistical information (such as direction
and speed). Deviations of the inferred statistics are inter
preted as atypical events, and therefore can be used to detect
and prevent dangerous situations. These options allow the
detection of sudden changes as well as atypical or threatening
events in the scene. Atypical or threatening events are gener
ally defined as deviations from the normal scene behavior or
have to be defined by a rule based scheme. Red light runners
and incident detection systems are an example for a self-
evident road traffic application.
The trajectories of street vehicles are smooth and homogene
ous over a large scale. Therefore, a mathematical description
by elementary functions is appropriate for these trajectories.
Thus, dramatic reductions of the bandwidths are achieved for
a full scene transmission. The basic step to determine the
driver intentions is to fit the trajectories to the analytical
This paper is organized as follows: After an overview of
situation analysis and atypical event detection the approach is
introduced. Then, an example installation is described and its
results are presented. The mathematical fundamentals of the
adaptation of formerly derived trajectories of turning vehicles
by hyperbolas, ellipsoids, spheres and straight lines are
sketched. The derived information is very comprehensive but
compact and permits downcast to other representations like
source destination matrices. The paper closes with a sum
mary and an outlook.
1.2 Situation Analysis and Atypical Event Detection
Scene description and automatic atypical event detection are
issues of increasing importance and an interesting topic in
many scientific, technical or military fields where complex
situations (i.e. scenes containing many objects and interac-
Corresponding author.