ATION 
D-scene, which 
entation and 3D- 
; projecting the 
convenient plane 
y a stereoscopic 
Xo point for 3D 
G) 
! X and Y axes 
surface using the 
lues. To assign a 
point (X,Y,Z) is 
ions. 
as are taken from 
ing» the object to 
  
Image 
a 
  
  
sible road area 
2000) it has been 
the calculation of 
in appearance of 
pourhoods of 3D- 
e. As orthogonal 
d by the surface 
ce is appeared on 
hood. 
duced to detection 
roperties on the 
straight-line edges 
| structures. Angle 
inctions of object 
form. 
hardware generally 
e convolutions and 
| makes it quite 
letection of corner 
To solve the problem mentioned above the orthophoto 
transformation in polar coordinate system (radial orthophoto) is 
introduced. The origin of polar coordinate system for left and 
right orthophoto is the appropriate camera focal plane. Point 
(o, R) of radial orthophoto is calculated as (4). 
  
Re fi-X V+ (1) 
X-X 
S 
(4) 
@ = arctan 
where — Xs, Ys - denote the projection center of the camera in 
road-based exterior coordinate system; 
R - denotes distance from the camera focal plane to 
point (X, Y); 
a- denotes the angle between optical axis of camera 
and line connecting point with camera. 
Pixel coordinates of radial orthophoto point are calculated as: 
(X, Y)-(R*sin(a)*Xs, R*cos(a)* Ys) (5) 
The invisible regions in radial orthophotos have a rectangular 
shape with vertical edges (see Figure 4). This important 
property allows to simplify the detection algorithm. Instead of 
finding 2D-corner shaped structures on differential orthophoto 
image the clusters of straight-line vertical edges have to be 
detected. As a result the detection problem can be reduced to 
1D-case by implementing such fast, hardware supported 
operation as vertical projection of image intensity. 
  
Figure 4. Radial orthophoto image of 3D-scene on Fig.1 
4. OBSTACLE DETECTION 
4.1 Obtaining obstacle features 
The main distinctive advantage of radial orthophoto is that it 
preserves vertical edges of 3D-object in the resulted orthophoto 
images. Since the vertical edges of object have a significant 
brightness variance in horizontal direction it allows to consider 
them as the most informative object features. 
To obtain edge points we introduce a "feature" image in such a 
way that obstacle edge is coded accordingly to the sign of 
brightness derivative in horizontal direction and brightness 
strength of the edge (Figure 5). The brightness derivative is 
calculated by using operator (6), that is the convolution of 
orthophoto image with special mask and addition of constant 
for negative value removal. Operator (6) gives reasonable 
compromise between the sharp response to edge points and 
sufficient averaging of the image for noise suppression. The 
brightness strength of the edge is a statistical characteristic 
based on analysis of edge points distribution. 
-j -1 0 1 ] 
w=] —1] 0 1-] 
1 (6) 
-i-L -1:0 1 1|1128 
20 
-] -1.0 1 1 
zl -i' 011 
  
(b) 
Figure 5. (a) derivative of orthophoto image in horizontal 
direction with use of operator (6); (b) resulted 
"feature" image 
4.2 Obstacle features detection 
In this work we use correlation based approach for obstacle 
features detection. 
Let the vertical intensity projection of the image be defined as 
(7): 
V(«)=2 165») 7) 
where V(x) — projection value; 
X, y — image coordinates; 
I(x,y) — intensity of the image at point (x,y). 
We consider the first derivative of such projection for left and 
right orthophoto images (Figure 6-a,b). The most significant 
correlation peak between projections represents the angular 
disparity of 3D-object (Figure 6-c). In other words it represents 
the certain distance where the maximal amount of obstacle 
features are found. 
—127—