2-3-3 
road at that instance. These have been 
illustrated in figures from 4 to 6. 
X 
Figure 3: Basic trigonometric concept 
underlying the extraction of road width. 
In real situation, this may not be applied 
directly as to non-uniformity of the vehicle 
run and road roughness. Fairly great amount 
of calibration needs to be done to get the 
system produce desirable results. 
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Figure 4: Grey scale image obtained through 
the ROMDAS video system with 
information overlay. ©HTC, 1998 
But once it has been calibrated for lens 
adjustment, extraction of road width from 
such an image requires 
a considerable amount of image processing 
with a efficient algorithm. The real scene 
roads contain objects like bridges, see fig.6, 
culverts and road markings. The processing 
algorithm should consider all these effects to 
produce desirable results. 
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Figure 6: Street furniture's, calling for an 
robust processing technique. © HTC, 1998 
This calls for an another aspect. It would be 
a herculean task to perform image 
processing for the entire sequence of 30fps 
digital video. Instead it has been proposed to 
build an intelligent image database to form 
images at certain interval, i.e,, every one 
second. At an optimal vehicle speed of 40 
km per hour would produce an image 
database at 11-m interval. The image 
capture may be changed with respect to 
users requirements. 
4 IMAGE DATABASE 
Figure 5: Ideal road texture for extraction. 
©HTC, 1998 
As outlined above the need for an 
comprehensive image database arises out of 
requirement. Since the processing is done at 
near real time, a large image database is 
created which needs to be indexed for 
continuous and proper operation of image 
processing, a 
One of the most important part of an image 
database system (IDBS) is a support of a 
query languages, and many researchers have 
proposed different ways of indexing. These 
becomes absolutely necessary as one go 
further to integrate the system with a