Information compensated can save bit-rates significantly. In video

Information is very important for video
content analysis. In surveillance video, usually the camera is stationary, and
the motions of the video frames are often caused by local motion objects. Thus
detecting motions in the video sequences can be utilized in anomalous events
detection, in sports video, the heavy motions are also related to highlights.
Motion estimation and compensation is the fundamental of video coding. Coding
the residual component after motion
compensated can save bit-rates significantly. In video sequences, the
motion configuration can be classified into two types: Local motion and Global
motion.

3.1   Global Motion

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Global motions in a video sequence are caused by camera motion, which
can be demonstrated by parametric transforms. The process estimating the
transform parameters is called global motion estimation 2. The global motions have certain consistence for
the complete frame as shown in below figure. The global motion in figure (a) is
a zoom out and that in figure (b) is a translation respectively.

We find that the motion track is
from outer to inner regions, which means
that the coordinates of a current frame t  can be generated in the inner regions of the
reference frame  V(t>v) . In figure 2,
the motion vectors in the motion field correspond to the global motion vectors
at the coordinates.

.
(a) Zoom-out
(b) Translation

Figure 2:
Global motion fields. (a)Zoom out and (b) Translation

Global motion
vector is the motion vector calculated from the estimated global motion
parameters. Global motion vector (, GMV ) for the current pixel with its coordinates () is determined as

=

GMV=                                                           (1)

3.2   Global
Motion Models

Global motion
can be represented by global motion models with several parameters. The
simplest global motion model is conversion with only two parameters. The
complex global motion model is quadric model with 12 parameters, which is
expressed as follows.

(2)

3.3   Global Motion Estimation (GME) approaches

Global motion estimation can be
carried out in pixel domain. In the pixel domain based approaches, all the
pixels are involved in the estimation of global motion parameters. There are
two short comings in pixel domain based approach: 1) it is very computational
intensive; 2) it is often sensitive to noises (local object motions). In order
to improve the convergence and speed up the calculation, coarse to fine
searching approach is often adopted. Moreover, the subset of pixels having the
largest gradient magnitude is adopted to estimate the global motion parameters
6. Sub-point based global motion estimation approaches are very effective in
reducing computational costs. To guarantee the accuracy of global motion
estimation, how to determine the optimal sub-sets are the key steps. Except the
pixel domain based global motion estimation, compressed domain based global
motion estimation approaches are also very popular.

3.4   Pixel domain based GME

In GME involving two image frames Ik and Iv (with k