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ISBN 978-952-5726-09-1 (Print)
Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 153-156
A New Method to Classify Shots in Basketball
Video
Yun Liu, Chao Huang, and Xueying Liu
College of Information Science and Technology of
Qingdao University of Science and Technology, Shandong province, P.R.China
Email:Lyun-1027@163.com
Email:{hchaopro, lxying2009}@qq.com
Abstract—As the foundation of the sports video annotation,
shots classification is presented in this paper. Using
non-supervised method the shots are clustered into defined
classes (in-play, close-up and free-throw) based on the
low-level features of the image (the main color and the
histogram). After comparing the clusters None Euclidean
Relational Fuzzy C-means (NERFCM) is applied to cluster
the shots. Experiments prove its efficiency and sensitivity.
Index Terms--shots classification; NERF C-mean; shots
boundary detection
I. INTRODUCTION
With the development of the computer and internet,
multimedia is widely used on the internet and becoming
the main carrier of the information, while people face a
lot of tremendous problems for its enormous size which is
how to use the data efficiently. Currently, most works
focus on specific sports games in order to investigate the
roles of different information sources or statistical
learning algorithms in structure analysis and semantics
extraction. The main challenge lies in the amount of
variation in low-level visual and auditory features, and
game-specific rules.
One of the popular approaches in sports video analysis
is event detection based method [1,2,3] . These methods aim
to find interesting events (e.g., shots in a basketball game)
by combining multiple cues (visual, audio, and textual
features). On the other hand, other efforts tried to find
interesting points of a video by modeling users’
excitement level [4,5] . Based on findings from
psychophysiology, human excitements are mimicked
using low–level features such as activity of motion, audio
level, and characteristics from video editing.
Although these systems have been successful in their
respective applications, shot type classification for sports
video is still an unsolved problem.
In this paper we propose a shot classify system to
cluster the shots based on the low-level features of the
frames. The rest of this paper is organized as the follows.
Section II is an introduction of the new method to find the
boundary of the video stream, and then features of the
frames are extracted in section III, in section IV 4
C-means algorithms are compared and the optimal method
is chosen to cluster the frames in a shot. Some shots
classes clustered are listed in Section V. In Section VI, we
This paper is supported by Natural Science Fund of Shandong
(Y2008G09).
.
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draw conclusions and discuss same future work.
II. SHOT BOUNDARY DETECTION
This section explains the algorithms for shot
boundary detection, which is the detection of the different
types of lenses in the video.
Shot boundary detection is usually the first step in
generic video processing. Although it has a long research
history, it is not a completely solved problem [6] . Sports
video is arguably one of the most challenging domains for
robust shot boundary detection due to following
observations:
1) There is strong color correlation between sports video
shots that usually does not occur in a generic video.
2) Sports video is characterized by large camera and object
motions. Pans and zooms are extensively used to track
and focus moving game objects, respectively.
3) A sports video clip almost always contains both cuts
and gradual transitions, such as wipes and dissolves.
At present, the research on the boundary detection fall
into two primary categories: detection based on the
compressed video and detection based on the primary
video. The later is faster in detection because it doesn’t
decompress the video, but for this very reason, the features
are limited and inaccurate [7] .
A. The histogram of the image
The algorithm basing on the histogram is developed in
the basis of pixels. In a shot the adjacent frames’ pixel
difference is tiny and so is the diagram’s standard
deviation. If the scene cuts from one shot to another, the
difference between them will be very large and so is the
standard deviation, for that reason we select standard
deviation to measure the shot transition.
In Fig.1 we have shown different types of the frames
we classify in our paper and their histograms. (a) and (b)
are the frames in play and their histogram, (c) and (d) one
are the frames in close up and their histograms, (b) and (c)
are the shot boundary frame. From the difference of the
histogram (g) we can conclude that histogram can be
expressed as the feature. At the boundary the standard is
very large as a single peak as shown in Figure1.
Generally speaking, the algorithm basing on the
histogram is simple to realize and has lower complexity,
moreover it can get a preferable result on a detecting video
abrupt change. However, compute pixel by pixel is