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Title
Close-range imaging, long-range vision



DEVELOPMENT OF TECHNIQUES FOR VEHICLE MANOEUVRES RECOGNITION
WITH SEQUENTIAL IMAGES FROM HIGH ALTITUDE PLATFORMS
T. Fuse*, E. Shimizu*, R. Maeda®
* Dept. of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan -
: (fuse, shimizu)@planner.t.u-tokyo.ac jp
Tokyo Gas Corporation, 1-5-20 Kaigan, Minato-ku, Tokyo, Japan
Commission V, Working Group IC V/III
KEY WORDS: Tracking, Monitoring, Image Sequences Analysis, Pattern Recognition, Planning, Navigation
ABSTRACT:
Traffic flow surveillance for traffic control and transportation planning require fixed-point continuous observation of exact dynamic
vehicle movement. High altitude platforms are expected to resul
continuous observation. These high resolution and continuous im
result, the high altitude platforms have a great potential with wide
trajectories. In this study, we explore the possibility of vehicle
images, which are on the assumption of the use of helicopters an
for vehicle manoeuvres recognition. In accordance with human
t in high spatial and time resolution images at specific areas for
ages certainly make observation of vehicle movement easier. As a
r scope of utilization to derive useful traffic information on vehicle
manoeuvres recognition with high resolution and time-serial aerial
d stratospheric platforms. Specifically, we develop a new technique
perception, we develop spatio-temporal clustering method, which is
composed of geometric correction, background subtraction, shadow detection, optical flow extraction, clustering and labelling.
Employing the two features, that are background subtraction value and optical flow, the pixels that are adjacent and have similar
features are grouped. Consequently, vehicle clusters are formed in the spatio-temporal image. We apply the spatio-temporal
clustering method to sequential images from a helicopter. The spatial resolution is 10cm, and the time interval of successive images
is 1/30 seconds, and the number of frames is 600. The method yields good result, and the rates of vehicle recognition are 100%. We
also confirm influence of spatial and time resolutions. Furthermore, we evaluate the accuracy of vehicle positions by comparing
with the vehicle positions produced manually.
1. INTRODUCTION
Traffic flow surveillance for traffic control and transportation
planning require fixed-point continuous observation of exact
dynamic vehicle movement. Traffic flow surveillance can be
conducted with kinds of beacons, GPS, cellular phones, video
cameras and manual counts. Among these ways, measurement
with video cameras is the most suitable for the continuous
observation of the exact movement of each vehicle, which is
important information for policy decisions on traffic problems.
There have been some attempts to study the measurement of
dynamic movement of vehicles with fixed video cameras (e.g.
Kamijo et al., 2000). Because the fixed video camera’s field of
view, however, was very narrow, the applications to traffic
engineering were limited.
On the other hand, images from high altitude platforms, such as
helicopters, satisfy the requirement, that is the observation of
exact dynamic vehicle movement in wide area. Moreover, in
recent years, a stratospheric platform system has been projected.
The stratospheric platform is being supposed to be kept at a
stratospheric altitude of about 20 km. It is intended to
contribute to telecommunication and the other different
purposes. One of them is utilization for earth observation.
These high altitude platforms are expected to result in high
spatial and time resolution images at specific areas for
continuous observation. These high resolution and continuous
images certainly make observation of vehicle movement easier.
As a result, the high altitude platforms have a great potential
with wider scope of utilization to derive useful traffic
information such as vehicle trajectories, namely locations,
velocities, acceleration and deceleration patterns, lane changes
and right and left turns in intersection. The information from the
vehicle trajectories can contribute to dealing with traffic
congestion, taking origin-destination demand proportions,
signal control, simulation of vehicle manoeuvres, and so on.
In this study, we explore the possibility of vehicle manoeuvres
recognition with high resolution and time-serial aerial images,
which are on the assumption of the use of helicopters and
stratospheric platforms. ^ Specifically, we develop a new
technique for vehicle manoeuvres recognition.
2. VEHICLE MANOEUVRES RECOGNITION
METHOD
2.1 Ordinary Object Tracking Method
The ordinary object tracking methods basically consist of two
components:
(1) extraction of objects in each image,
(2) object tracking by making a correspondence of extracted
objects in successive images.
The extraction methods which employ the information of
movement are divided into subtraction-based approach (e.g.,
Betke et al, 2000; Haritaoglu et al., 2000) and optical flow-
based approach (e.g., Ben-Ezra et al, 2001; Medioni et al.,
2001; Smith and Brady, 1994).
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