7A-4-5 
information on the multiple image matching method, interested 
readers can refer to Tao et al., (1997) and Tao (1997). 
In the current implementation, the search window size is set as 
25H x 17V pixels, considering the georeferencing errors. The 
correlation coefficient threshold is set as 0.70. If the number of 
match points is more than half of the total points applied, the 
corresponding line segment exists. Since the match points may 
not be distributed along a perfect line, a line approximation 
algorithm using the least median square criterion (Rousseeuw, 
1987) is used to filter out the spurious points and determine the 
best line. 
The above method is also applied to the other consecutive 
images (forward and backward images) to find 
correspondences. Finally, a straight-line constrained 
photogrammetric triangulation can be employed to obtain a 
more reliable and accurate result of 3-D line reconstruction. 
This feature triangulation algorithm has been described in a 
number of publications (Mikhail, 1994; Wilkin, 1996). 
Current image pair 
Figure 7. Multinocular line matching 
5. TEST RESULTS AND EVALUATION 
A batch processing method was implemented to detect the 
transportation objects based on a map database. A stereo image 
sequence, consisting of 729 images was used for the test. The 
images were collected by the VISAT system in the city of 
Laval. Totally, 56 desired vertical objects, mainly including 
light poles and traffic signs were chosen and projected onto the 
image sequence. After the processing of verification, 52 objects 
were verified to exist using the method described above, and 
four objects were not able to be detected. Based on the results of 
manual check, one object was actually missing and the rest 
three were blocked by other objects (moving vehicles). This test 
demonstrates that the developed method is able to achieve a 
successful rate of 94.6% (=53/56). 
A typical example of the whole process is given in Figure 3a - 
3e. The Figure 3a is a stereo image pair. There are four vertical 
objects of interest (marked in the image). Object No.l is to be 
examined. Figure 3b is the output of edge detection using the 
six directional masks. All the possible vertical edges have been 
detected. Figure 3c shows the effect of edge thinning. It can be 
seen that a very large number of undesired edges have been 
filtered out, while the vertical edges have been kept. A refining 
process is conducted using the Sobel gradient operator. More 
accurate direction values of existing edges are calculated and 
only those edges whose directions are vertical with a tolerance 
of 15° remain. As we shall see in Figure 3d, the edge points of 
road boundary in the left image are mostly eliminated since 
these edges are not vertically oriented. The effects of line 
formation are shown in Figure 3e. Since we are interested in the 
distinct features, only those line segments longer than 50 pixels 
are recorded in the line file and displayed. This result 
demonstrates that the new line grouping algorithm functions 
very well, and is able to bridge the gaps to form a line segment 
from edges. In this example, the left-bottom line segment was 
chosen to perform feature correspondence and multinocular line 
reconstruction. The position information of this line segment is 
used to update its database. 
Other examples are given in Figure 8 and Figure 9. The desired 
features were detected successfully. The processing windows as 
well as extracted line features have been overlapped in these 
images. For a comparison, in Figure 9, the entire images were 
processed using the line detection method described above. It 
can be seen that the use of predicted windows generated using 
the existing map databases will largely eliminate the 
ambiguities in the process of line detection, i.e., line features 
existing outside of the processing window will not affect the 
final results. 
6. CONCLUDING REMARKS 
A new approach for the verification and updating of transportation 
objects with vertical line features using road image sequences is 
proposed. Based on the evaluation of practical testing results, the 
following conclusions can be drawn: 
• Using multiple constraints developed from the mobile mapping 
system, the approach is able to detect and reconstruct the 
transportation objects in a highly automatic and fairly reliable 
manner. 
• The prior position information of the desired objects derived 
from the existing databases greatly simplifies the detection of 
objects and makes the approach much reliable. 
• Abundant image information of the objects offered by the 
georeferenced multiple image sequences allows a robust 
implementation of the object extraction and reconstruction 
algorithms. 
• It has been demonstrated that the developed algorithms of line 
feature extraction, line grouping, line feature correspondence, 
multiple image matching, and multinocular line reconstruction 
functioned successfully. 
This approach has gained a great attention of the user community. 
Requests for a further test and licensing of the software is being 
processed. A future research will be aiming at the detection and 
recognition of arbitrarily shaped objects along road corridors. In this 
case, color image information as well as the prior knowledge of the 
desired objects froman existing database will be utilized. 
REFERENCE 
Chen, H. H., and T. S. Huang, 1990. Matching 3-D Line 
Segments with Applications to Multiple Object Motion