Image Processing Reference
There are many videos about sports. There is a large need for content-based video retrievals.
method to retrieve shots including a similar motion based on the similarity of the motion of a
have a good performance using both the correlation of motions and the correlation of textures.
This paper proposes the method to retrieve the plays using only motion compensation vec-
tors in MPEG videos. Using multiple features, the performance increases. Many works try to
index sport videos using the motions in the videos. Many works try to understand the pro-
gress of games with tracking the players. Many of the works use the motion vectors in MPEG
videos. They succeed to find camera works. They are zoom-in, zoom-out, pan, etc. However,
no work retrieves a play of a single player from only motions directly. Of course, camera
works have an important role in understanding videos. Sound also has some roles in under-
standing videos. Many works use camera works and sound for understanding sport videos.
Those feature-combining methods have some successes about retrieving home runs and other
plays. However, those works did not success to retrieve plays from only motions. Recently,
In this paper, we try to propose a method that retrieves similar plays only from motions.
With the help of texture, the performance of similar play retrieval increases. We showed that
the combination both of motions and textures show beter performance about similar play re-
of playing fields changes with the change of seasons. The motion based method can works
with textures, sound, and camera works. In a previous paper, we proposed the method to es-
The method gets the motions from motion compensation vectors in MPEG videos, and makes
the one-dimensional projections from the X motion and Y motion. The motion compensation
vector exists at each 16 × 16 pixel's square. Very sparse description of motions is the motion
description made from the motion compensation vector. The one-dimensional projection rep-
resents the motions between a pair of adjacent frames as a one-dimensional color strip. The
method connects the strips in the temporal direction and gets an image that has one space di-
mension and one time dimension. The resulting image has the one-dimensional space axis and
about all pixels, but the temporal slice method does only about the cross-sections. We call this
image as a space-time image. Using the images, the method retrieves parts of videos as fast as
image retrievals do. We can use many features defined on images.
Our previous work uses a single template space-time image, and retrieves similar plays as
and correlation, we can use different templates. Using different templates, they need not to be
a same length. In our old works, for retrieving the pitches in baseball games, the best length of
template is 20 frames. However, the experiments are restricted.
A long space-time image template is good for retrieving the same play in the template.
However, in a similar play retrieval, the long template of a space-time image is not robust
about the change of the duration of a play. A short space-time image template is robust about
the change of the duration in a similar play retrieval. However, a short space-time image They
plate is weak in the discrimination power.