7/25/2019 10.1.1.218.101

1/5

The 5thUmpire: Crickets Edge Detection System

R. Rock, A. Als, P. Gibbs, C. Hunte

Department of Computer Science, Mathematics and PhysicsUniversity of the West Indies (Cave Hill Campus)

Barbados

AbstractThe game of Cricket, and the use of technologyin the sport, have grown rapidly over the past decade.

However, technology-based systems introduced to

adjudicate decisions in run outs, stumpings, boundary

infringements and close catches are still prone to human

error, and thus their acceptance has not been fully

embraced by cricketing administrators. In particular,

technology is not currently employed for bat-pad decisions.

Although the Snickometer may assist in adjudicating such

decisions, it depends heavily on human interpretation. The

aim of this study is to investigate the use of Wavelets indeveloping crickets edge-detection adjudication system.

Live audio samples of ball-on-bat, and ball-on-pad events

from a cricket match, will be recorded. Wavelet analysis

and feature extraction will then be employed on these

samples. Results will show the ability to differentiatebetween these different audio events. This is crucial to

developing a fully automated system.

Keywords: Cricket, Wavelets, Edge-detection, featureextraction

I. Introduction

Sports play a major role in generating entertainmentrevenue. At the 2000 Sydney Olympic Games, theorganizing committee generated an income of US $1.756

billion [1]. In 2009, the Indian Premier League (IPL) offeredpaychecks as high as US$1.55 million to top class cricketersfor a five week contract [2]. Consequently, to deter event-fixing and ensure legitimate results, it is not surprising thatthe use of technology in sports has steadily increased overthe years and now plays a major role in adjudicating theoutcome of events. Moreover, it has become crucial in

protecting the livelihoods of sportsmen who make a livingfrom sports. Incorrect decisions in sporting events couldaffect a players confidence, the outcome of a game and evenend a players sporting career. This view was supported byMr. Nariman Jamshedji Nari Contractor while addressingthe Fourteenth Frank Worrell Memorial Lecture at theUniversity of the West Indies. He stated that the use oftechnology increases the likelihood of a correct decision bythe referee and this greatly assists the players in earning alivelihood from their sports.

Unfortunately, Cricket is no exception and the prevalenceof incorrect decisions, in spite of the exposure to varioussporting technology, continues to be disconcerting to its widefan base. Reasons range from the inefficiency of theequipment (e.g. blind spots due to players or umpires

blocking the view of cameras used in decision makingprocesses) to human error by the on-field umpires. Ininternational cricket, a Third Umpire consults with the on-field umpires using wireless technology. More specifically,the Third Umpire uses television replays in situations, suchas disputed catches and boundary infringements, toappropriately advise the on-field umpires. The Third Umpireis also called upon to adjudicate on run out decisions.

There are also a number of devices being used to assistthe umpires in making decisions and for the entertainment of

television audiences.One such device Hawk

-Eye, is a

computer system which traces a balls trajectory, with aclaimed accuracy of 5mm, and sends the data to a virtual-reality machine [3]. Hawk-Eye uses six or more computer-linked television cameras situated around the cricket field of

play. The computer acquires the video in real time, andtracks the path of the cricket ball on each camera. These sixseparate views are then combined to produce an accurate 3Drepresentation of the path of the ball. The system was firstused during a Test match between Pakistan and England atLord's Cricket Ground, on 21 April 2001, in the TV coverage

by Channel 4 [3]. Since then it has been an indispensabletool for cricket commentators around the world. It is used

primarily by the majority of television networks to track the

trajectory of balls in flight, mostly for analyzing Leg BeforeWicket (LBW) decisions. In this case, Hawk-Eye predictsthe most likely path of the ball, and determines if it would goon to hit the wicket. Although Hawkeye is very accurate inmeasuring the actual path of a ball, when it comes to

predicting the future path of the ball, such as in LBWdecisions, it is not as clear. If the ball is heading to the pitch(ground), Hawk-Eye cannot determine if it will skid a bitmore than normal or hit a crack, bit of grass, or worn patchof the pitch. The predicted path of the ball is based on theaverage and expected pathway [3] and does not take intoeffect any deviation that can be caused by imperfections onthe ground surface.

Another item of equipment being used in cricket is theSnickometer (also known as snicko). This was invented byEnglish Computer Scientist, Allan Plaskett, in the mid-1990s. The Snickometer is composed of a very sensitivemicrophone, located behind the stumps, and an oscilloscope(wirelessly connected) which displays traces of the detectedsound waves. These traces are recorded and synchronizedwith the cameras located around the ground. For edge-decisions, the oscilloscope trace is shown alongside the slowmotion video of the ball passing the bat. By the transientshape of the sound wave, the viewer(s) first determineswhether the noise detected by the microphone coincides with

7/25/2019 10.1.1.218.101

2/5

the ball passing the bat, and second, if the sound appears tocome from the bat hitting the ball or from some other source.This technology is currently only used as a novelty tool togive the television audience more information regarding ifthe ball actually hit the bat. Umpires do not enjoy the benefitof using 'snicko' but must rely instead on their senses of sightand hearing, as well as personal judgment and experience. Inmany instances, there are coinciding events that may be

confused with the sound of ball-on-bat. These include the bathitting the pad during the batmans swing or the bat scuffingthe ground at the same time the ball passes the bat. Theshape of the recorded sound wave is the key differentiator asa short, sharp sound is associated with bat on ball. The bathitting the pads, or the ground, produces a flatter soundwave. The signal is purportedly different for bat-pad and

bat-ball however, this is not always clear to the natural eye[4]. The aim of this paper is to employ wavelet analysis andfeature extraction to differentiate between bat-on-ball and

bat-on-pad audio sounds in cricket. Results could then beemployed in an automated decision making process. It isexpected that this will give teams a fairer chance on theoutcome of a match (game) by minimising the number of

decision errors currently observed in the game.

II. BackgroundIt is well known that the continuous wavelet transform

(CWT) may be used to analyze audio signals. The CWTprovides another view of temporal signals as it transformsthe regular time vs. amplitude signal to time vs. scale, wherescale can be converted to a pseudo-frequency. This methodallows one to examine the temporal nature of audio eventsand the corresponding frequencies involved. In essence, thecorrelation values, produced during the transformation

process, provide critical information on the characteristics ofthe signal. By exploiting these characteristics a distinction

can be made between different audio events. Significantapplications of this new dimension are widely reported inthe literature. Lambrou et al used the wavelet transform toextract statistical features from audio data to successfullydistinguish between three different musical styles of rock,

piano and jazz [5] In another documented application, thewavelet-packet transform was used to extract spatiotemporalcharacteristic features for vehicular detection [6].

Ting et al introduced a novel time-frequency basedpattern recognition technique for a proposed effective cricketdecision making system using snicko-signals [7]. In theirwork, experiments were conducted to simulate and recordsnickometer signals using a PC microphone with the help of

RAVEN LITE software. A simple time-frequency basedpattern recognition technique was developed and tested.

It should be noted that this software employs the short-time Fourier transform (STFT) to generate the time-frequency data used in the pattern recognition analysis.

It is believed that this is the first time that waveletanalysis has been employed in classifying the audio signalsrecorded during the game of cricket. This innovation will bevery useful in live broadcast of cricket match for the benefitof audience and adjudicators.

III. MethodologyThe equipment setup shown in Fig. 1 was tested at

various local hard-ball cricket matches throughout Barbados.This setup is identical to that used for international matches.More specifically, the microphone transmitter is covered in asmall hole directly behind the stumps. The receiver and thelaptop are assembled inside the players pavilion andrecordings are made using the laptops sound recorder.

The key specifications for equipment used in recordingthe audio data are listed in Table 1.

The received audio signals were first digitised using a 16-

bit pulse coded modulation (PCM) scheme at a sample rateof 44,100 kHz and then stored as a stereo .WAV file on alaptop computer for offline wavelet analysis. The primarysoftware tool for this analysis was MATLAB. It must benoted that there are many available wavelet transforms andscale ranges that can be used for testing. However, thewavelet used for analysis in this project is the Bi-orthogonal3.3, and the scale chosen for this particular wavelet is fromone to four hundred and fifty. Fifty-two samples were taken;twenty-six of ball hitting bat and twenty-six of ball hitting

pad. Samples included deliveries from both fast- and slow-bowlers and the batsmens equipment (e.g. pads, bat) alsovary in make and model.

WAVELET

TRANSFORM

FEATURE

EXTRACTION

FEATURE

CLASSIFICATION

SIGNAL

RESULT

STUMP

MICROPHONE

&

TRANSMITTER

RECEIVER LAPTOP

Figure 1: Schematic of experimental setup

7/25/2019 10.1.1.218.101

3/5

(a)

(b)

TABLE I. EQUIPMENT PARAMETERS

EQUIPMENT SPECIFICATIONS

Shure SLX14/84 WirelessLavalier MicrophoneSystem

WL184 Supercardiod

Lavalier Condenser Mic:

Supercardioid pickup patternfor high noise rejection and

narrow pickup angle

SLX1 Body pack

Transmitter:

518 - 782 MHz operatingrange

SLX4 Wireless Receiver:

960 Selectable frequenciesacross 24MHz bandwidth

Mobile Precision M6400Notebook Computer

Precision M6400, Intel Core 2Quad Extreme EditionQX9300 2.53GHz, 1067MHZ

IV. Results

Recordings of the impact of ball hitting bat and ball hittingpad were successfully compiled. In many cases, attempts tocategorise these signals by visual inspection of the data inthe time domain, proved to be inconclusive as they aresimilar in appearance. This is highlighted in Figures 2(a)and 2(b). An adjudicating official could not reliably use thismethod to determine the source of the recorded noise as isdone using the Snickometer.

However, results showed that when using the wavelettransform, there is a noticeable difference between ballhitting bat and ball hitting pad. Figures 3 and 4 show the

results from the continuous wavelet transform of the twoprevious sound files. Note that the x-axis shows how thetransform varies across time and the y-axis shows thecoefficient value of the wavelet and the z-axis shows thescale. It is observed from these figures that for the ball

hitting pad, the best correlation value is at a higher scale (i.e.lower pseudo-frequency) than for the ball hitting bat, wherethe best correlation value also observed that the correlationvalue for a ball hitting bat (12) was higher that the value for

ball hitting pad (2.5).

Time (samples)

Scale

CoefficientV

alues

Figure 3: 3D wavelet transform of ball hitting pad

Time (Samples)

Scale

CoefficientV

alues

440

330

220

110

Figure 4: 3D wavelet transform of ball hitting bat

Figure 2: Sound samples taken of (a) ball on pad,(b) ball on bat

7/25/2019 10.1.1.218.101

4/5

In Figure 5, twenty-six wavelet coefficient values are plotted

against their corresponding pseudo-frequencies for both ball-on-bat and ball-on-pad data categories. Observe that thereare two general areas which represent contact between

bat/ball, and ball/pad, respectively. Moreover, there is alinear partition separating the two categories of signals. This

partition, which is represented as a line drawn between thetwo categories of signals serves as a possible decision

boundary for classification.

V. Conclusion

Results showed that viewing a sound file in the time domainproved to be ambiguous when adjudicators need to

determine the difference between key sounds in cricket suchas ball hitting bat and ball hitting pad. When the sound filesare analyzed using the wavelet transform, noticeabledifferences between the two signal classifications arerevealed by the correlation values of the wavelet transformand the corresponding pseudo-frequency at the pointsinterest. These differences were plotted on a graph for easyviewing. We are now closer to designing a fully automatedsystem which, when given a sound file of a noise, candetermine if it was bat-on-ball or ball-on-pad. Thisdistinction provides the means for which an automation toolcan be developed. Such a tool will be very useful in live

broadcast of cricket match for the benefit of audience and

adjudicators.

The wavelet chosen for testing is just one of many availablewavelets. Future research will be carried out to determinewhich wavelet gives optimum results. In this regard itshould be noted that it is possible to design a wavelet which

best represents a particular sound. The scale range of thewavelets employed also needs to be examined, as changingthe scale range of the wavelet may produce improvedresults. Therefore, an optimum scale range for each wavelet

must also be established. Also, other characteristics given

by the wavelet transform will be investigated to determine ifthere are other noticeable differences between the signals.This work will also be enhanced to a real-time applicationand extended to include bat-pad adjudication decisions.

Acknowledgment

The authors would like to acknowledge Mr. SimonWheeler (Executive Producer/Director of TWI and IMGmedia company), Mr. Mike Mavroleon (Senior EngineerIMG media) and Mr. Collin Olive (Asst. Sound EngineerIMG media) for their direction in acquiring the equipment

needed to record the data samples used in this paper. Theseare the persons responsible for technological aspects of thebroadcast of international cricket (i.e. snickometer, hawk-eye, TV replays).

References

1. Olympaid, Beijing. 2011.Profit or Loss?China InternetInformation Center, [Online], last cited 2011, January,01], Available:http://www.china.org.cn/english/sports/111340.htm

2.

Peter J. Schwartz, Chris Smith, 2011, The World's Top-Earning Cricketers, [Online], last cited 2011, January,01], Available:http://www.forbes.com/2009/08/27/cricket-ganguly-flintoffl-business-sports-cricket-players.html.

3. Wood, Rob J., 2005, Hawk-Eye System in Cricket,[Online], last cited 2010, January, 21]. Available:http://www.topendsports.com/sport/cricket/equipment-hawkeye.htm.

Figuure 5: Difference between bat on ball (stars), and ball on pad (squares).

7/25/2019 10.1.1.218.101

5/5

4. Wood, Rob J., 2005, Cricket Snicko-Meter, [Online],

last cited 2010, January, 21, Available:http://www.topendsports.com/sport/cricket/equipment-snicko-meter.htm.

5. Lambrou, T., P. Kudumakis, R. Speller, M. Sandler,and A. Linney. Classification of audio signals using

statistical features on time and wavelet transformdomains, In Proc. IEEE International Conference on

Acoustics, Speech and Signal Processing, 1998.

6. Schclar, A., A. Averbuch, N. Rabin, V. Zheludev, andK. Hochman. A diffusion framework for detection ofmoving vehicles, Digital Signal Processing, vol 20,no 1, pp. 111-122, Feb. 2009.

7. Ting, S., and M. V. Chilukuri. Novel patternrecognition technique for an intelligent cricket decision

making system. In Proc. IEEE Instrumentation andMeasurement Technology Conference, I2MTC '09,

2009.