Goal Event Detection in Soccer Match

1. Goal Event Recognition
in Soccer Match
Presented By : Dharmesh R. Tank
M Tech-CE(Sem III)
Guided By : Prof D.S.Pandya
(UVPCE-CE Department)
27/12/2014

2. Outline
Objective
Why digital Video?
Video Summarization
Concentrate on Sport Matches
Literature Review
Proposed Algorithm
Inference using Bayesian Network
Why Bayesian Network ???
Experiment Results
Conclusion & Future Work

3. Area of Research: Goal Event Detection
Semantic Video Analysis
Automatic Concept Extraction
Content Based Search Engine
Video Indexing
Video Summarization
Objective :

4. Why Digital Video?
Audio/visual data in a binary format.
Information is represented as a sequence of zeroes and ones, rather than as a
continuous signal as analog.
It’s Include video recording, editing and playback technology through digital.
It can be stored on random access media, whereas analogue video is generally
stored sequentially on magnetic tape.
It can be duplicated without loss of quality which is important for editing
applications.
Advantages : Ease of manipulation, Preservation of data, Compression

5. Video Summarization
Short summary of the content of a longer video.
Classified into Highlights and Cause Summary.
Highlights, contains the whole summary of interesting and attractive parts.
Cause Summary, conveys the highest semantic meaning of the content.
Valuable semantics generally occupy only a small portion of the whole
content.
Applications : Medical image processing, Human tracking, Traffic system
monitoring and Sports video analyzing.
High semantic events : Goal / Non-Goal event detection

6. Concentrate on Sports Matches
Game analyses are increasingly valuable for professional teams to remain
competitive.
Conventional methods of post-game analyses often fail to analyze and
visualize the complex spatio temporal patterns of the Sport sufficiently.
Sports video analysis is to provide “Assistance For Training”.
Ever-green field and attraction for big spending each year.
Need to summarize the play tactics from video streams
Classifying a play sequence into an existing tactic pattern and recognizing
unknown patterns.
Examples : Cricket tactics, Soccer, American Football, and Tennis.

7. Literature Review
Video summarization techniques are mainly categorized into two classes:
1) Segmentation based video summarization
(i) Shot Based Segmentation : Video can divided into frames on the
basis of individual shots and find such a
representative frame of each shot, which
may well describe the video.
(ii) Cluster Based Segmentation : It generated the cluster of such
redundant frames and obtaining as the
need of shot.
2) Event based video summarization

8. Event based Video Summarization
Events Description
Replay Event
Detection
Detecting slow motion pattern and logos at certain duration.
Score Board
Detection
Identifying the caption that appears at the bottom part of the
frame and remaining present for a minimum duration.
Goal Mouth Appearance of goal mouth region in the frames will indicate
attack or important situations like : goal, Foul and attack.
Close-up View As close-up of the player is displayed longer after goal event,
It’s a unique feature for distinguishing between goal and
attack events.
Audience View When interesting event occur, this event come into the frame.

9. Ball Detection in Soccer
Soccer game is the most spatial-temporal nature.
The information about ball position during the match will improve soccer
analysis.
Different aspects to be considerable like size, area, colour, centroid, and
longevity features are used to discriminate the ball from other objects.
Two way to extract the ball detection in a soccer match :
In first, all the moving regions are detected making use of a background
subtraction algorithm
Second that, a connected components analysis detects the blobs in the image.

10. Goal Event
Reference : Event Detection Based Approach for Soccer Video Summarization Using Machine learning by Hossam M.
Zawbaa, Nashwa El-Bendary, Aboul Ella Hassanien, and Tai-hoon Kim, Cairo University, Faculty of Computers, Cairo, Egypt

11. Proposed Algorithm Level
High Level
Middle Level
Low Level
Goal Event
Ball
Detection
Edge
Colour
Non Goal Event
Goal Mouth
Detection
Shapes
Centroid

12. Data Flow of Algorithm
Input Video Frames
Event
Segmentation
Event
Categorization
Low Level
Features
Mid-Level
Features
Bayesian
Inference
System
High-Level
Semantic
Non-Goal
Event
Goal Event

13. Inference : Using Bayesian Network
 BN is a graphical model for Bayesian classifier which is called an optimal classifier
because of its minimum classification error.
 In general graph must be, DAG(Direct Acyclic Graph)
p(X1, X2,....XN) =  p(Xi | parents(Xi ) )
 This modelling is applied for training and inference of hypotheses which are
conditionally dependent to each other, and provide accurate results for discrete
values.
 In Bayesian inference network, each of these hypotheses is effective with a
specific probability for the existence of attack situation or goal event.
Reference: A Brief Introduction to Graphical Models and Bayesian Networks, By Kevin Murphy, 1998.

14. Why Bayesian Network???
Compact representation of joint probability distributions
Generate optimum predication estimation
Encode deterministic relationships between nodes
Causality property (also referred as causation)
Explaining way property (also referred as selection bias)
Conditional Independence
Bayesian Inference

15. Example
Cloudy
Sprinkler Rain
Wet Grass

16. BN Proposed Structure
Goal Mouth
Detection
Ball
Detection
Replay
Duration
Goal

17. Experiment Result : Dataset 1
Fig:1 (a)Original Image Fig:1 (b) Crop & Resize Image Fig: 1 (c) RGB to Gray Image
Fig:1 (d) Gray to BW Image Fig:1 (e) BW to Dilation Image Fig:1 (f) Object Detection (Ball)

18. Experiment Result : Dataset 2
Fig:2 (a) Original Image Fig:2 (b) Crop & Resize Image Fig:2 (c) RGB to Gray Image
Fig:2 (d) Gray to BW Image Fig:2 (e) BW to Dilation Image Fig:2 (f) Object Detection(Ball)

19. Experiment Result : Dataset 3
Fig:3 (a) Original Image Fig:3 (b) Crop & Resize Image Fig:3 (c) RGB to Gray Image
Fig:3 (d) Gray to BW Image Fig:3 (e) BW to Dilation Image Fig:3 (f) Object Detection(Ball)

20. Conclusion & Future Work
The important actions about the events of the game, Highlights and Summary
are important aspect for semantic analysis and observation.
We are trying to reduce the gap between low level features and the high level
semantics via mid-level feature.
Here we found some more effective and encourageous results about ball
detection.
Now by fed up this results into Bayesian inference, trying to extract the high
level semantic goal event.

21. References
1. Hossam M. Zawbaa, Nashwa El-Bendary, Aboul Ella Hassanien, and Tai-hoon Kim, “Event Detection Based Approach for
Soccer Video Summarization Using Machine learning”, Cairo University, Faculty of Computers and Information, Cairo, Egypt
2. A.Ekin, A.M.Tekalp and Rajiv Mehrotra, “Automatic Soccer Video Analysis and Summarization”, IEEE Transaction on
image processing, Vol. 12, No. 7, July 2003.
3. S. Alipour, P. Oskouie, A.M.E.Moghadam, “Bayesian Belief Based Tactic Analysis of Attack”, Electrical, Computer & IT
Eng. Dept. Islamic Azad University of Qazvin, Iran
4. M.H.Kolekar, S.Sengupta and G. Seetharaman, “Semantic concept mining based on hierarchical event detection for soccer
video indexing”, Academy Publisher Journal of Multimedia, Vol. 4, No. 5 (2009), 298-312, Oct 2009.
5. M.M. Naushad Ali, M. Abdullah-Al-Wadud and S.L. Lee, “An Efficient Algorithm for Detection of Soccer Ball and Players”,
Onlinepresent.org, Vol. 16, 2012.
6. D.S. Pandya and M.A. Zaveri, “A novel framework for semantic analysis of an illumination-variant soccer video”, EURASIP
Journal on Image and Video Processing, springer Open Journal, Nov. 2014.
7. C. Cotsaces, N. Nikolaidis, I. Pitas,"Video Shot Detection and Condensed Representation: A Review", IEEE Signal
Processing Magazine, pp.28-37, 2006.
8. K. Murphy, “A Brief Introduction to Graphical Models and Bayesian Networks”, 1998.
9. S.F. de Sousa Junior, A. Araujo and David Menotti, “An Overview of Automatic Event Detection in Soccer Matches”,
Applications of Computer Vision (WACV), 2011 IEEE Workshop, pp. 31 – 38, Jan. 2011.
10. http://people.cs.ubc.ca/~murphyk/Bayes/bayesrule.html

22. Thank You