Video summarization is a useful technique in present world to view all the important aspects of a lengthy video. Video summarization extracts important representative shots from the video and it conveys all the semantics of the entire video in a short span of time. In sports, it plays a very important role to generate highlight of the game video spanning over many hours. Hence we propose an algorithm for automatic summarization of lengthy hockey game videos. In this method, different efficient algorithms are devised to find shot detection, penalty corner and penalty stroke detection, Umpire detection and foul detection. The method also detects all the replay shots along with logo shots. The method finally combines all the important events detected to form a summarized video. It also allows the user to create customized video summary containing user preferred events. The method is tested with many international Hockey match videos and the average efficiency is found to be 0.88.

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International Journal of Advanced Research in Engineering and Technology
(IJARET)
Volume 6, Issue 11, Nov 2015, pp. 59-71, Article ID: IJARET_06_11_006
Available online at
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=11
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
© IAEME Publication
___________________________________________________________________________
AUTOMATIC SUMMARIZATION OF
HOCKEY VIDEOS
Hari R
Department of Computer Science, University of Kerala
ABSTRACT
Video summarization is a useful technique in present world to view all the
important aspects of a lengthy video. Video summarization extracts important
representative shots from the video and it conveys all the semantics of the
entire video in a short span of time. In sports, it plays a very important role to
generate highlight of the game video spanning over many hours. Hence we
propose an algorithm for automatic summarization of lengthy hockey game
videos. In this method, different efficient algorithms are devised to find shot
detection, penalty corner and penalty stroke detection, Umpire detection and
foul detection. The method also detects all the replay shots along with logo
shots. The method finally combines all the important events detected to form a
summarized video. It also allows the user to create customized video summary
containing user preferred events. The method is tested with many international
Hockey match videos and the average efficiency is found to be 0.88.
Key words: Video Summarization, Colour Segmentation, SSIM, Optical
Flow, Hough Transform.
Cite this Article: Hari R. Automatic Summarization of Hockey Videos.
International Journal of Advanced Research in Engineering and Technology,
6(11), 2015, pp. 59-71.
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=11
1. INTRODUCTION
Content based video retrieval technique helps the user to browse the preferred events
in the sports video in a short span of time. Enormous research works are going on in
the automatic highlight generation of various sports video that includes all the
relevant events happening in the game. Since the sports videos are in unscripted
pattern, we have to extract out the video scenes containing few semantically related
and continuously record image sequence [1] [2]. Most of the sports video
summarization works exploits the visual and audible features of the game video [3]-
[6] such as detecting the whistle sounds, ground excitement, text boxes etc. in the
video. Another approach is to exploit the semantic descriptions of the sports video,
which can be done either by using a Bayesian network [7], Ontology modeling [8] [9],

2. Hari R
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classification of temporal structure of the game [10] [11], etc. A novel motion
analysis method for generating highlights of racket sports video is proposed in [12],
which exploits the player behavior and audience response and it was tested on
broadcast tennis and badminton videos. A team-sports video summarization based on
the knowledge about displayed content and the individual preferences of the user
which was experimented on soccer, basketball and volleyball videos is presented in
[13]. In [14], the storyboard summary generation of snooker video is carried out using
a hierarchical event representation framework and importance based selection
algorithm. Highlight based summary generation of cricket videos by the detection of
events like FOUR, SIX, OUT, RUN etc. can be done using visual and aural features
as well as semantic description techniques of the game video [15] – [18]. From all
these, it is clear that novel attempts were carried out to summarize sports videos like
soccer, snooker, tennis, cricket etc. whereas efforts for the detection of events in a
Hockey game is still in the infant stage.
Hockey is a game played between two teams, each having eleven players
including the goal keeper. The players hit a small leather ball with a curved stick and
if the player of team A strikes the ball and if it is made to enter into the goal post of
team B, then team A is said to score a ‘goal’. The Hockey field is rectangular in shape
having a large center line which is termed as the ‘Halfway line’. In addition to this,
the field also consists of side lines and goal lines on either side of the center line
nearer to the goal posts that are situated at the two ends of the field as shown in Figure
The goal keepers of each team will take the position in front of the goal post and they
will always try to block the ball hit by the players of the opposite team from entering
his/her goal post. There will be two Umpires in the field wearing a jersey with a
colour other than the colour of team players’ jersey, who continuously monitor the
game and decide penalty serving and foul by displaying green, red and yellow cards
to the players in the game.
The present work concentrates on highlighting the major three events in the
Hockey game video like ‘goal’, ‘penalty corner’, ‘penalty stroke’ and major ‘fouls’.
The organization of the paper is as follows: Section 2 describes the proposed method
along with the details of the techniques used for shot detection and classification,
event detection and summarization. Section 3 discusses the experimental results
obtained and section 4 concludes the work.
2. PROPOSED WORK
In this work, we propose a new methodology for automatic summarization of Hockey
game video by using the inherent features of the game as well as the game dependent
structured motion pattern and recording fashion of the cameras. In a Hockey match,
the most important event is scoring of a goal as well as the attempt to score a goal. In
addition, penalty stroke, penalty corner and serious fouls can also be treated as
important events. Here, the methodology developed can automatically create a
summarized video containing all the above events. The overview of the system is
given in figure 1.
Initially, all the frames of the Hockey video are undergone preprocessing steps for
enhancing the features by removing noises. These frames are then analyzed to find the
shot boundaries for grouping the frames into different shots. After this, the replay
shots are found out and removed from further processing. The frames of the
remaining shots are then analyzed to find the features contained in them for their
classification into the following categories.

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1. Long shots: The shots recorded by the camera without zooming such that the
prominent area in each of the frame in a shot is occupied by the field.
2. Close-up shots: These shots contain frames within which the face of the player or
Umpire appears.
3. Umpire shots: Here the frames in the shot contain the Umpire, whose presence
can be identified by the unique color of the Umpire uniform that can be easily
distinguished from the players.
4. Goal post/Goal mouth shots: These shot frames contain goal post.
5. Logo shot: In all game videos, computer generated logo graphics are flashed
frequently. Normally, the logo shots are displayed at the beginning and end of
replay shots.
6. Replay shot: Slow motion replays are always telecasted just after the occurrence
of an important event.
Figure 1 Overview of the system
Now the occurrence pattern of the above said shots are examined to deduce the
presence of important events in those shots. These events are finally combined to
create summarized Hockey video. In the following sections the details of the
techniques used to detect and classify the shots are explained.
(a) (b) (c)
(d) (e)
Figure 2 Sample Frames of (a) long shot, (b) close-up shot, (c) logo shot, (d) umpire
shot and (e) goal post shot
Shot
detectionInput
video
frames
Shot classification
Long
shots
Replay
shots
Goal post
shots
Audience
shots
Umpire
shots
Event detection Summari-zed
video

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2.1. Shot detection
In Hockey video, multiple cameras are used to capture the events. In this context, a
shot can be designated as a group of frames continuously recorded by a single camera
and hence all the frames in a shot exhibit structural and spatial similarity. Structural
similarity index measure (SSIM) [19] is used to find the similarity between adjacent
frames. If the similarity value between adjacent frames is above a threshold value,
then those frames belong to a single shot. To proceed further, the groups obtained so
far will be termed long shots, close-up shots, Umpire shots and goal post shots.
2.1.1 Detection of Logo shot
The duration of logo shots are less than a second. Compared to other shots in the
video, this shot is of very small. Moreover the colour contrast in this shot is found
higher than that of other shots. Hence these two cues are used to distinguish the logo
shots from others. All the shots whose duration is less than a second, i.e., shots
containing less than 30 frames, are considered for selecting as logo shots. In addition,
the contrast in the selected frames is calculated and if this is greater than that of
frames in other shots, the shot is finally designated as logo shot.
2.1.2 Detection and removal of replay shots
In Hockey video, the important events are always followed by its replay in slow
motion. These replay shots are always telecasted between two logo shots. Hence all
the shots coming between two adjacent logo shots with duration less than 2 minutes
are considered as replay shots and they are removed immediately from further
processing, i.e., all shots excluding the logo shots and replay shots are given to next
modules for further processing. Since this is preceded by important shots, once a
replay shot is detected by the system, the system tags the preceding shot as important.
2.2. Shot classification
All the shots except replay shots and logo shots are thoroughly analyzed for detecting
the presence of objects like field, goal post, players and umpires. Based on these
features, shots can be classified into one of the following using different techniques.
 Long shot: In long shots, the frames contain a distant view of the playground. Hence
the major portions of the frames in these shots are occupied by the field. Using color
segmentation technique, we can detect the field and if its area is greater than ¾ of the
frame, after the removal of minor objects in the frame using morphological
operations, shot is tagged as long shot. In different hockey matches, synthetic fields
of different colures are used. Our system automatically selects the major colour in the
first few frames of the video as the field colour for processing the remaining frames.
 Close-up shot: During a game, the camera focuses either on a single player or players
or the umpire depending on the events occurring in the shot. For example, when a
player scores a goal, the camera starts focusing on the goal post, then on the player
who scored the goal and the players who gathered around the scorer to express their
immense emotions. To detect a close-up shot, our method tries relies on two methods,
namely field colour detection and skin colour detection. When a close up view is
displayed, the field is either missing or partially present in the frames. Hence the ratio
of field colour pixel to total pixel in the frame is computed and if it is less than or
equal to ¼, the shot can be considered as a candidate shot for close up shot selection.
To confirm its candidature as close up shot, skin colour segmentation is carried out
next to detect the face as well as the hands of the players. If the method can detect the
skin within the frames of the above shot, it is finally tagged as a close up shot.

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 Umpire Shot: The game is controlled by Umpires, wearing distinct colour dress.
Using colour segmentation technique, the Umpires can be easily distinguished from
the players of both the team. Since the Umpires wear different colour uniforms in
different matches, the system needs human intervention to select the colour of
Umpires’ uniform in the initial phase.
(a) (b)
Figure 3 The output of Umpire detection procedure. (a) Original image, (b) Colour
segmented image.
 Goal post shots: Two goal posts are located at the two ends of the field, which are
characterized by vertical strips / poles that are connected by a horizontal strip. Hough
transform is used to detect the vertical and horizontal strips of the goal post and
morphological operations are carried out to find the rectangular goal post. If any one
of the frames in a shot contains the goal post, the shot is classified as goal post shot.
(a) (b) (c)
(d) (e)
Figure 4 Illustration of goal post detection process. (a) original goal post frame, (b)
binary image of (a), (c) image after performing morphological operations on (b), (d)
lines detected by Hough transform in (c) and (e) the detected boundary lines of the
goal post
2.3. Event detection
In Hockey video, the major events include scoring of goals, attempt to score a goal,
foul, penalty stroke etc. All these events in the video are characterized by unique
features like presence of goal post, signals of umpire and the emotions shown by both
the players and spectators. In the proposed work, the gestures of umpires and the
presence of objects or events are taken into account for detecting important events and
thereby creating effective summary of the Hockey video.
2.3.1. Goal detection
If a goal is scored, the shot contains the goal post and hence goal event is always
included in the goal post shot. The immediate shot coming after this contains the
close-up view of the player or players showing emotions. The goal event is always
followed by a set of replay shots also. The system checks the occurrence of this
sequence and if the sequence is detected, the system selects the goal post shot as goal
shot or goal event and the shot is also tagged with the name ‘goal’. Hence the same

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shot is having two tags, namely ‘goal post’ and ‘goal’. A failed goal attempt also have
the same structure, since in the summarization point of view, both the events i.e.,
scoring a goal and failed attempt to score a goal are equally relevant. Hence the
technique can be well adopted in this work.
2.3.2. Penalty corner/ Penalty stroke detection
If a foul occurs in the penalty area of a team, the Umpire awards a penalty corner or
penalty stroke for the opposite team. In all these events, the players take a preparation
time before performing it. During this time, the camera focuses on the penalty area
wherein the goal post exists. To detect the penalty corner, our system estimates
motion in all goal post shots. If the motion of such a shot is very less and below a
threshold and if the shot spans over a minimum of three seconds, it can be taken as a
penalty corner. In this work, optical flow method [20] is used to extract the motion in
the goal post shots. If the above conditions are met in a goal post shot, it is named
with ‘Penalty corner/ Penalty stroke’ tag. If the sequences club with that of goal
detection sequence, then the penalty corner may end in goal event. In this case the
same shot has three tags, namely, ‘goal post’, ‘Penalty corner/Penalty stroke’ and
‘goal’.
2.3.3. Foul detection
The Umpire signals the occurrence of fouls by showing either green or yellow or red
cards. Of these both red and yellow cards indicate major fouls. Hence our system
detects the fouls indicated by both yellow and red cards and the respective Umpire
shot and its preceding shot in which the foul occurs are tagged as ‘foul’ shot. The
method to detect these foul events is implemented through the following steps.
1. Skin color cum yellow and red color segmentation is carried out in frames of all
Umpire shots.
2. The resulting frame in step (1) is analyzed for the presence of yellow or red color.
If either yellow or red color is present, step (3) is performed.
3. Connected component analysis is carried out to find whether the yellow or red
color of the card appears immediate to skin color which is contributed by the
hand of the Umpire.
(a) (b) (c)
Figure 5 Illustration of foul detection through Umpire signal. (a) the Umpire shows
yellow card after a foul committed by a player, (b) the colour segmented image of (a),
(c) connected component analysis is performed on (b) to verify the presence of yellow
colour in hand.
2.4. Video summarization
All the shots are detected and tagged with the name of the event contained in it as
mentioned in the above sections. In this module, the method selects all the shots
tagged with important events. This includes penalty stroke, penalty corner, goal and
major fouls. Then all these events are stitched together in the same order as they
appear in the original video to form the summary of the video. In addition, the method

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also enables the user to create his own summary, containing those interested events
like all goal post shot alone or all goal shots using the tags of the shots.
3. EXPERIMENTAL RESULTS
The method is experimented with a large number of Hockey matches being played in
many places under both sunlight and flood lights. All the matches are in MPEG
format recorded in full HD (1920x1080) resolution recorded at 30 fps. For the
evaluation purposes, clips from first half of eight games are randomly chosen and the
general details of the games are given in table 1.
Table 1 General details of the game dataset
Game Match Place Date Details
Length of
the video
clip (min)
H1
England Vs
Pakistan
Bhubaneswar India 7/12/2014 1st round match 38:58
H2
Belgium Vs
Australia
Bhubaneswar India 7/12/2014 1st round match 41:57
H3
Netherlands Vs
India
Bhubaneswar India 9/12/2014 1st round match 35:55
H4
Argentina Vs
India
Bhubaneswar India 7/12/2014 1st round match 34:48
H5
New Zealand Vs
England
Mendoza, Argentina 4/12/2014
Quarter Final
(Women’s
World Cup)
39:26
H6
Germany Vs
India
Delhi
India
13/1/2014 Final 36:03
H7
Argentina Vs
Australia
Bhubaneswar India 11/12/2014 Quarter Final 2 37:38
H8
Belgium Vs
India
Bhubaneswar India 11/12/2014 Quarter Final 4 17:43
The results for individual modules can be analyzed separately. For the evaluation,
recall, precision and F-score measures are used.
The performance of the shot detection module is evaluated by , , and
parameters. If is the number of correctly detected events verified
manually , is the number of events detected by this method and is the actual
number of events identified by the human, then we can define these parameters as,
The value of all the parameters range from 0 to 1. A high value indicates
the effectiveness of the method in finding correct shots. The reflects the
ability of the method to avoid false shot detection.

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3.1. Shot boundary detection
In the games taken for experimentation, the total shots is the summation of shots
containing the game, logo shots, one or more replay shots included between a pair of
logo shots. The shot detection results are given in table 2.
Table 2 Shot boundary detection results
Game Ms Ts Cs
Recall
(Cs/Ms)
Precision
(Cs/Ts)
F-score
H1 297 289 271 0.91 0.94 0.92
H2 368 370 339 0.92 0.92 0.92
H3 324 332 308 0.95 0.93 0.94
H4 318 314 296 0.93 0.94 0.93
H5 418 426 375 0.9 0.88 0.89
H6 340 345 322 0.95 0.93 0.94
H7 315 322 287 0.91 0.89 0.9
H8 217 223 202 0.93 0.91 0.92
Note: Ms- Total number of shots manually detected, Ts- Total number of shots detected by the
method and Cs-Number of correctly detected shots
From the table, it can be found that the method very well detects the shot
boundaries leaving an average recall, precision and f-score values of 0.93, 0.92 and
0.92 respectively.
3.2. Replay detection
Since the logo shots are computer generated graphics shots, the detection of logo
shots and replay shots can easily be carried out with high accuracy. Each replay may
contain multiple shots featuring the view of the same shot in different angles. The
replay results are given in table 3. It can be seen from the table that most of the replay
shots are correctly detected by the system, proving the efficiency of the replay
detection algorithm.
Table 3 Replay detection results
Game MR TR CR
Recall
(CR/MR)
Precision
(CR/TR)
F-score
H1 34 35 33 0.97 0.94 0.95
H2 44 43 40 0.91 0.93 0.92
H3 50 50 47 0.94 0.94 0.95
H4 23 24 22 0.96 0.92 0.94
H5 36 38 33 0.92 0.87 0.89
H6 32 35 31 0.97 0.89 0.93
H7 27 28 26 0.96 0.93 0.94
H8 20 20 18 0.9 0.94 0.95
Note: MR-Total number of replays manually detected, TR- Total number of replays detected
by the method and CR-Number of correctly detected replays.
3.3. Goal post shot detection
Since the goal post or goal mouth is clearly demarked by the white strips of the post,
the detection rate is also very high. The results of goal post shot detection are given in
table 4. In certain games the recall and precision values are less, since the frames
including the side view of the goal post are not detected correctly by the method and

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in certain other cases, the goal post is partially covered by the players, making the
detection of the goal post difficult.
Table 4 Goal post shot detection results
Game MGP TGP CGP
Recall
(CGP /
MGP)
Precision
(CGP /TGP)
F-score
H1 68 70 65 0.96 0.93 0.94
H2 77 82 70 0.91 0.85 0.88
H3 60 57 52 0.87 0.91 0.89
H4 66 65 61 0.92 0.94 0.93
H5 95 98 89 0.94 0.91 0.92
H6 46 45 42 0.91 0.93 0.92
H7 62 65 58 0.94 0.89 0.91
H8 29 30 27 0.93 0.9 0.91
Note: MGP-Total number of goal post shots manually detected, TGP- Total number of goal post
shots detected by the method and CGP- Number of correctly detected s goal post shots.
3.4. Umpire shot detection
Color segmentation algorithm works well in identifying the umpire in the game from
players. Since the umpires wear different color dress in different games, the color has
to be selected manually in the system. In some games the color of the umpire dress
matches with that of the goal keeper, very few false alarms occurred. Once the
Umpire shot is identified, again color segmentation is used to find the different cards
like red, yellow or green cards. The results of Umpire shot detection are given in table
5.
Table 5 Umpire shot detection results
Game MRS TRS CRS
Recall
(CRS/MRS)
Precision
(CRS/TRS)
F-score
H1 24 25 23 0.96 0.92 0.94
H2 32 30 29 0.91 0.97 0.94
H3 28 32 25 0.89 0.78 0.83
H4 52 55 49 0.94 0.89 0.91
H5 44 43 42 0.95 0.98 0.96
H6 26 26 24 0.92 0.92 0.92
H7 40 51 32 0.8 0.63 0.7
H8 19 25 16 0.84 0.64 0.73
Note: MRS-Total number of umpire shots manually detected, TRS- Total number of goal post
shots detected by the method and CRS- Number of correctly detected s goal post shots.
In games H3, H7 and H8, we can see that the recall and precision values are less.
This is because, in these games the colour of Umpires’ uniform matches with that of
the goal keeper of a playing team
3.5. Goal detection
A goal shot is identified by the occurrence of the following sequences – goal post shot
with a close up shot. Here all goals are detected fairly and hence good recall and
precision as given in table 6.

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Table 6 Goal detection results
Game MG TG CG
Recall
(CG/MG)
Precision
(CG/TG)
F-score
H1 5 6 4 0.8 0.67 0.73
H2 5 5 4 0.8 0.8 0.8
H3 0 1 - - - -
H4 2 4 2 1 0.5 0.67
H5 3 4 3 1 0.75 0.86
H6 3 2 2 0.67 1 0.8
H7 2 3 2 1 0.67 0.8
H8 1 2 1 1 0.5 0.67
Note: MR- Total number of goals manually detected, TR-Total number of goals detected by
the method and CR- Number of correctly detected goals
3.6. Penalty corner detection
Our system detects the penalty corner with high degree of accuracy, which is given in
table 7.
Table 7 Penalty corner detection results
Game MPC TPC CPC
Recall
(CPC/MPC)
Precision
(CPC/TPC)
F-score
H1 2 4 2 1 0.5 0.67
H2 4 5 3 0.75 0.6 0.67
H3 3 2 2 0.67 1 0.8
H4 1 3 1 1 0.33 0.5
H5 7 8 6 0.86 0.75 0.8
H6 5 4 4 0.8 1 0.89
H7 2 3 1 1 0.67 0.8
H8 2 4 2 1 0.5 0.67
Note: MPC- Total number of penalty corners manually detected, TPC- Total number of penalty
corners detected by the method and CPC-Number of correctly detected penalty corners.
Most of the penalty corners are detected correctly and few false alarms are due to
the motion of the camera as well as the focusing of the camera on the players instead
of the goal post.
3.7. Foul detection
In all the games considered, the major fouls are signaled by the Umpire using yellow
cards only and our system has detected all the fouls correctly leading to recall and
precision values of 1. In the games chosen experimentation, the method detected all
the yellow cards displayed by the Umpire. But in some cases, the Umpire showing the
cards will not be focused by the camera, leading to the failure of the methods.
Table 8 Foul detection results
MF TF CF
Recall
(CF / MF)
Precision
(CF / TF)
F-score
3 3 3 1 1 1
Note: MF - Total number of yellow/red cards manually detected, TF - Total number of
yellow/red cards detected by the method, CF - Number of correctly detected yellow/red cards

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3.8. Summarization Result
For evaluating the result of summarization, we have considered the number of events
included in the automatically created video. In this video all the important events
related to scoring of a goal, penalty corner and fouls are included. Summarization
efficiency is evaluated using recall, which is defined as the ratio of total number of
events correctly detected by the method to the total number of events in the game. The
final results are given in table 9.
Table 9 Results of Summarization
Game
Total number of
events in the game
Total number of events
correctly detected
Recall
H1 8 7 0.88
H2 11 8 0.73
H3 3 3 1
H4 3 3 1
H5 10 9 0.9
H6 8 6 0.75
H7 4 3 0.75
H8 3 3 1
From this, we can see that the method provides average recall of 0.88, showing
that the method is highly efficient in generating summary with all the major events..
4. CONCLUSION
A new method for extracting important events in Hockey game videos to create
effective summary of the game is proposed. Even though many different methods are
available for summarizing other sports like soccer, basketball, cricket, etc, very few
efforts were made to summarize the hockey video game. In our method, all the
inherent features of the game are considered for extraction of important events. The
method involves many sub-modules and efficient algorithms are devised in each of
these modules. Experimentally all these modules are tested and the result obtained
from the modules are found promising. An average summarization efficiency of 88%
is obtained. In future the score card extraction and audio commentary detection can
also be incorporated to increase the efficiency of the system.
REFERENCES
[1] Chong Wah Ngo, Yu-Fei Ma and Hong-Jiang Zhang, Video Summarization
and Scene Detection by Graph Modeling, IEEE Trans. on Circuits ans
Systems for Video Technology, Vol. 15, No. 2, Feb. 2005, pp. 296-305.
[2] Xinghao Jiang, Tanfeng Sun, Jin Liu, Juan Chao and Wensheng Zhang, An
Adaptive Video Shot Segmentation Scheme Based on Dual- Detection
Model, Journal on Neurocomputing, 116, 2013,pp. 102-111.
[3] Baoxin Li, Hao Pan and Sezan, I., A General frame work for sports video
summarization with its application to soccer, IEEE International Conference
on Acoustics, Speech and Signal Processing, (ICASSP ’03), 2003, vol. 3, pp.
169-172.
[4] Dian Tjondronegoro, Yi-Ping Phoebe Chen and Binh Pham, Integrating
Highlights for More Complete Sport Video Summarization, Published by the
IEEE Computer Society 2004 IEEE, pp.22-37.

12. Hari R
http://www.iaeme.com/IJARET/index.asp 70 editor@iaeme.com
[5] A. Hanjalic, Generic Approach to highlighte extraction from a sport video,
ICIP 2003.
[6] F. Coldefy, P. Bouthemy, M. Betser and G. Gravier, Tennis video abstraction
from audio and visual cues, Proc. of IEEE 6th
workshop on Multimedia
Signal processing, Sep. 2004,pp. 163-166.
[7] Xueming Qian, Huan Wang, Guizhong Liu and Xingsong Hou, HMM based
soccer video event detection using enhanced mid-level semantic, Multimedia
Tools Appl. (2012) 60:233-255.
[8] Lamberto Ballan, Marco Bertini, Alberto Del Bimbo and Giuseppe Serra,
Semantic annotation of soccer videos by visual instance clustering and
spatial/temporal reasoning in ontologies, Multimedia Tools Appl (2010) 48:
pp. 313-337.
[9] Jian-quan Ouyang and Renren Liu, Ontology reasoning scheme for
constructing meaningful sports video summarization, IET Image Processing,
2013, vol.7, Issue 4, pp. 324-334.
[10] Takahashi, Y. , Nitta, N. and Babaguchi, N., Video Summarization for Large
Sports Video Archives, Proc. of IEEE International Conf. on Multimedia and
Expo, ICME 2005, pp. 1170-1173.
[11] Shu-Ching Chen, Miami F. L. ,Min Chen, Chengcui Zhang, Mei-Ling Shyu ,
Exciting Event Detection Using Multi-level Multimodal Descriptors and
Data Classification Eighth IEEE International Symposium on Multimedia,
2006. ISM'06, pp. 193-200.
[12] Guangyu Zhu, Qingming Huang, Changsheng Xu and Liyuan Xing, Human
Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video,
IEEE Trans. on Multimedia, vol.9, Issue 6,2007, pp. 1167-1182.
[13] Fan Chen, Damien Delannay and Christophe De Vleeschouwer, An
Autonomous Framework to Produce and Distribute Personalized Team-Sport
Video Summaries: A Basketball Case Study, IEEE Trans. on Mutimedia, vol.
13, No. 6, December 2011, pp. 1381-1394.
[14] Dr. K. Devaraju. Effect of S.A.Q Training on Vital Capacity among Hockey
Players. International Journal of Advanced Research in Engineering and
Technology, 5(1), 2015, pp. 102 - 105.
[15] K. Devaraju and A. Needhiraja, Prediction of Playing Ability in Kabaddi
from Selected Anthropometrical, Physical, Physiological and Psychological
Variables Among College Level Players, International Journal of
Management, Volume 3, Issue 2, 2012, pp. 150 - 157.
[16] Matthew L. Parry, Philip A. Legg, David H. S. Chung, Iwan W. Griffiths and
Min Chen, Hierarchichal Event Selection for Video Storyboards with a Case
Study on Snooker Video Visualization, IEEE Trans. on Visualization and
Computer Graphics, vol. 17, No.12, December 2011, pp.1747-1756.
[17] Namuduri, K., Automatic Extraction of Highlight from a Cricket Video
Using MPEG 7 Descriptors, IEEE First International Conference on
Communication Systems and Networks and Workshops, 2009, pp. 1-3.
[18] A. Kokaram and P. Delacourt, A New Global Motion Estimation Algorithm
and its Application to Retrieval in Sports Events, IEEE fourth Workshop on
Multimedia Signal Processing, 2001 pp. 251 – 256.
[19] N. Harikrishna, Sanjeev Satheesh, S. Dinesh Sriram and K. S. Easwarakumar,
Temporal Clssification of Events in Cricket Videos, IEEE National
Conference on Communications, 2011, pp. 1-5.

13. Automatic Summarization of Hockey Videos
http://www.iaeme.com/IJARET/index.asp 71 editor@iaeme.com
[20] Maheshkumar H. Kolekar and Somnath Sengupta, Semantic concept mining
in cricket videos for automated highlight generation, Multimedia Tools and
Applications, Springer, vol. 47, No. 3, May 2010, pp. 545-579.
[21] Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh and Eero P.
Simoncelli, Image Quality Assessment: From Error Visibility to Structural
Similarity, IEEE Trans. on Image Processing, vol. 13, no. 4, pp. 600-
612,April 2004.
[22] B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence,
16:185–203, Aug. 1981.
[23] K. Devaraju and A. Needhiraja, “Prediction of Kabaddi Playing Ability from
Selected Anthropometrical and Physical Variables Among College Level
Players”, International Journal of Advanced Research in Engineering &
Technology (IJARET), Volume 3, Issue 1, 2012, pp. 118 – 124.