×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Title
Proceedings International Workshop on Mobile Mapping Technology
Author
Li, Rongxing

vectors in each vehicle was (1) 5.3 pixels/frame, (2) 2.0
pixels/frame and (3) 0.9 pixels/frame, respectvely. The
smoothness constraint smoothes variety of direction of
flow vectors, so the magnitudes of flow vectors are under
estimated.
Data at a pixel
and Sun, 1983). Figure 13 shows the result which was
solved by spatial local optimization method with
neighborhood of edge. However, the result were not
precise and dense sufficiently to be employed for 3D
reconstruction and structure from motion.
Figure 10: Unstability of the Solution by Multispectral
Constraints Method.
The result which was solved by spatial local optimization
method (Figure 2) was better than by other gradient-based
approaches. In the spatial local optimization method,
there were pixels at which flow vectors could not be
estimated. It is called as aperture problem. Figure 11
illustrates the aperture problem. It shows a moving plane
of constant brightness. When we see the plane through
the aperture, we cannot recognize the moving of the plane.
Figure 12: Vehicle Tracking Employing Spatial Local
Optimization Method.
-m -m
- ***** ■ ' ■ ■
lOpixels/frame
Figure 11: Aperture Problem.
Consequently, flow vectors at all feature points cannot be
obtained by employing the basic methods of gradient-
based approach and their combined methods. Hence, it is
difficult to analyze details of vehicle motions taking into
the shape of the vehicles account by the flow vectors which
are solved by basic methods of gradient-based approach.
On the other hand, only vehicle tracking on the 2D screen
can be achieved. Figure 12 shows a frame of vehicle
tracking. Defining the black regions as the clusters,
vehicles were tracked.
By the way, flow vectors were extracted near edges, and no
flow vectors inside the vehicles. According to this result,
the edges were defined as spatial neighborhood, and then
spatial local optimization method were applied (Davis, Wu
Figure 13: Spatial Local Optimization Method with
Neighborhood of Edge.
CONCLUSION
The conclusions of this paper are as follows:
(1) It is difficult to estimate precise and dense optical flow
by the basic methods of gradient-based approach and
their combination, when sequential images are took at
an interval about 1/30 seconds.