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CAR COLLISION AVOIDANCE SYSTEM
BASED ON ORTHOPHOTO TRANSFORMATION
S. Yu. Zheltov, A. V. Sybiryakov, O. V. Vygolov
State Research Institute of Aviation Systems (GosNIIAS), 7, Victorenko str., Moscow, Russia
zhl@gosniias.msk.ru
Commission V, WG V/1
KEY WORDS: Image Processing, Object detection, Three-dimensional scene, Stereoscopic, Model, Real-time.
ABSTRACT:
In this paper we propose the complex method of 3D-objects detection based on special orthophoto transformation. Three-
dimensional scene is registered by calibrated stereo-system with known relative and interior orientation parameters. The objects of
interest are located on the surface of known analytical model. The paper shows that orthophoto transformation represents each 3D-
object raised above the surface as a 2D-structure with the predicted properties on the resulted orthophoto images. To find these
structures the correlation based approach is used in common with statistical analysis of special vertical projections of orthophoto
images. The method provides simple description of the detected objects such as distance, width and height above the surface. In the
paper implementation of the method is considered for car collision avoidance system.
1. INTRODUCTION
The problem of 3D-objects detection comprised in observation
scene often occurs in various machine vision applications.
Typical example of the problem is obstacle detection for car
safety systems (Bertozzi and Broggi, 1977; Zheltov,
Sybiryakov, 2000) that is the objective of this paper.
In the considered work the car-based calibrated stereo-system,
with known relative and interior orientation parameters, is used
for real-time detection of obstacles that are located on a road
ahead of the car and in the same road lane. The road model
acquisition is performed by computer vision methods followed
by 3D-reconstruction.
The obstacle detection method is based on orthogonal
projections (orthophotos) of the road to some convenient plane
with use of left and right stereo images.
Orthophoto represents the 3D-object raised above the road as a
2D-structure with the predicted properties on the resulted
images. Transformation of orthophoto image into polar
coordinate system is suggested to simplify the detection method
by introducing hardware supported projection technique. The
3D-object corresponds to simple 2D-clusters of vertical
straight-line edges on the transformed orthophoto images.
These clusters are found by implementing correlation based
approach and statistical analysis of special vertical projections
of orthophoto images.
The obstacle detection method consists of number of simple
single-pass image processing operations such as convolution,
projections computation and LUT-based transformations. To
obtain orthophotos in polar coordinate system a piece-wise
bilinear transformation is applied. The paper shows that
transformed orthophotos have common properties of both
orthophotos and stereo images.
The method provides simple description of each obstacle such
as distance, width, position in a lane and height above the road.
Kalman filtering gives relative speed of the obstacle.
In the paper the examples of moving car detection based on the
developed method are shown.
2. OBTAINING A ROAD MODEL
In this work the situation is assumed that 3D-object, registered
by car-based calibrated stereo system with known relative and
interior orientation parameters, stands on smooth road ahead of
the car (Figure 1).
Figure 1. Example of 3D-scene (left image of stereo-pair)
Usual way of background surface model acquisition is the least
square method of fitting to the set of points, which are exactly
not belonging to the detectable object. In this work the 3D
marking points are considered as such a set.
Let the relative coordinate system of left and right cameras be
defined as follows: the system origin is in projection center of
the left camera, the X axis coincides with the photography basis
direction, the Y axis is parallel to the CCD-matrix column
accepted as initial (average CCD-matrix column) and is
directed to the sky, the Z axis coincides to the main optical axis
of the left camera and directed opposite to car move.
To obtain a road model the road-based exterior coordinate
system (XYZ) is introduced so that the surface has 2.5D-view
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