The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
Because of the above factor it inescapable to cause the error
matching. Although it takes lot of matching method to ensure
the correspondence of moving object feature points such as
taking the image correlation based feature, relaxation matching
method based matching sustain, image relation based gray, and
matching in local area etc, but there are still some error
matching points. For example, in the above experiment, from
the step CD to step®, the correct feature points ’ amount which
obtained is decrease as the process. For example the feature
points in CD feature extraction from same sequence image, there
are 17 feature points, the right matching points are 15, that is the
number, in the process®, of different sequence stereo matching
feature points arel5, but after finishing the 3D object-side
feature point correspondence, the correct number of feature
points only are 13, the final correspondence is about 76.5%. So
it add feature matching robust arithmetic in the program, such
as using the mature bundle adjustment theory in
photogrammetry field, to auto discover the error in feature
matching or correspondence, and form a feed back control
process, it worth to further study.
4. CONCLUSIONS
In order to realize the location and tacking of the three-
dimensional object based on points feature, this paper discusses
the most challengeable problem in this process that is the
features correspondence problem. The algorithm of double
restriction matching combining motion and stereo image
matching is presented after analysis the character of sequence
(motion) matching and stereo matching in the process. Take the
advantages of high overlap of motion video frequency images,
The final correspondence is about 75.6%, which is validated by
real data experiment. It can meet the requirements of the 3D
motion object tracking location. It will be the future study to
give the further optimizing and robust algorithm of feature point
correspondence.
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Project supported by the National Science Foundation of China
(No.40171080)