Goal Event Recognition in Soccer Match

Presented By : Dharmesh R. TankM Tech-CE(Sem III)

Guided By : Prof D.S.Pandya(UVPCE-CE Department)

27/12/2014

OutlineObjective

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

Area of Research: Goal Event Detection

Semantic Video Analysis

Automatic Concept Extraction

Content Based Search Engine

Video Indexing

Video Summarization

Objective :

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

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

Concentrate on Sports Matches

Game analyses are increasingly valuable for professional teams to remaincompetitive.

Conventional methods of post-game analyses often fail to analyze andvisualize 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 recognizingunknown patterns.

Examples : Cricket tactics, Soccer, American Football, and Tennis.

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

Event based Video SummarizationEvents 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.

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.

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

Proposed Algorithm Level

High Level

Middle Level

Low Level

Goal Event

Ball

Detection

Edge

Colour

Non Goal Event

Goal Mouth

Detection

Shapes

Centroid

Data Flow of AlgorithmInput Video Frames

Event Segmentation

Event Categorization

Low Level Features

Mid-Level Features

Bayesian Inference

System

High-Level Semantic

Non-Goal Event

Goal Event

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.

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

ExampleCloudy

Sprinkler Rain

Wet Grass

BN Proposed Structure

Goal Mouth Detection

Ball Detection

Replay Duration

Goal

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)

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)

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)

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.

References1. 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

Thank You