Figure 3. Example of marker 
Table 2 shows the result of the method with markers. 
Recognizing the markers increased in extracted and tracked 
feature points successfully. Moreover, the ground plane was 
estimated with stable by the marker arrangement (Figure 4). 
The effect of the markers was confirmed. 
Table 2. Result of feature points tracking with markers 
  
  
  
  
  
  
  
  
  
  
initial number of feature points 1027 
0 (fine) 500-750 
number of feature points | 1 150-230 
in image pyramid level 2 25-50 
3 (coarse) 0-1 
final number of feature points 1027 
  
  
Figure 4. Model arrangement with markers 
On the other hand, some limitations of the method were 
understood. Because the baseline between adjacent video 
frames is quite short, it was difficult that accuracy of the 
exterior orientation and three dimensional coordinates 
estimation of the feature points increased. 
4.2 Refinement of Feature Points Tracking 
As previously discussed, feature points extraction and tracking 
are important for the final result. Recently, more sophisticated 
feature points extraction algorithms have been developed. One 
of the most reliable algorithms is SURF (Speeded-UP Robust 
Features) (Bay et al, 2008). The SURF algorithm uses box 
filter, which approximates Hessian-Laplace detector, for making 
integration images. The integration image improves 
computational speed. Additionally, points included in a certain 
radius circle are added for calculation of norm, and then 
orientation is adopted with maximum norm. According to 
above mentioned feature, the SURF is robust against scaling 
and rotation. Finally, image is divided into 4 x 4 block, and 
then differences of features are represented as 64 dimension 
SURF features (Figure 5) by using those gradient and amplitude 
(Sk, Sch, ll, 3-10) 
  
  
Figure 5. Concept of SURF 
We compared stability of the FAST and the SURF in outdoor 
environment, and confirmed that results of the SURF are more 
robust than ones of FAST. According to the results, this paper 
employs SURF as feature points extraction algorithm. 
Even if the SURF is applied to feature points extraction and 
matching, incorrect matching points are still exist. Additionally, 
feature points matching is refinement by using not only adjacent 
frames also sequential frames. Firstly, extracted feature points 
are searched in sequence between adjacent frames. After the 
matching process is conducted within a certain number of 
frames, position of feature points are re-projected into first 
frames. If the displacement between first and last position of 
the points is larger than a threshold, the feature points are 
discarded. With the result of the matching, three dimensional 
coordinates of the feature points can be calculated. If the depth 
of the points is larger than a threshold, the feature points are 
also discarded. Finally, the remaining points are accepted as 
feature points. Figure 6 shows an example of results of 
matching refinement. 
   
Figure 6. Matching refinement with SURF 
  
176 
4.3 
sca 
aux 
poi 
can 
pos 
cor 
the 
the 
seq 
wh: 
app 
con 
the 
app