A-Eye is a novel technology that automate the role of third umpire in cricket..Currently 3rd umpiring is used which is time consuming..A-Eye is accurate and efficient

1. A-EYE: AUTOMATING THE
ROLE OF THE THIRD UMPIRE
IN THE GAME OF CRICKET
Presented by
Aneesh.T.G
Roll no:6
S7 IT

2. ABSTRACT
 In cricket ,currently for giving umpiring decisions like
stumping and run out ,the third umpire has to review various
angular video footage
 This process consume around one minute which disrupts the
pace of the game
 In A-Eye a set of autonomously filmed run-out videos are
applied
 Efficient as third umpire and accurate
 Used to estimate a rating for the field umpires 2

3. INTRODUCTION
 Artificial Eye (A-Eye), which exploits image processing
techniques
 Illustrate the working of various architectural components of
A-Eye and algorithm for automating the Run-Out decision.
 Conclusions along with the future work.
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4. EXISTING SYSTEM
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 Currently third umpiring is used.
Disadvantage
Disadvantage
 While the third umpire is making his decision, all the players
have to wait for it, and the game stops entirely .This causes
 It disrupts the playing rhythm of the players.
 It leads to a loss of playing time for both the teams.
 Third umpires are quite fallible.

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6. PROPOSED SYSTEM
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 A-Eye: Automating the role of the third umpire.
Advantage
 Robust
 Minimize the decision time

7. SYSTEM ARCHITECTURE
 GUI 1 is initially used to load
and perform some pre-processing
tasks
 GUI 2 is then used to detect the
motion at the wicket and the
crease
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Architecture of A-Eye

8. 1. Process video module
 A complete video player is implemented within GUI 1
 It allows users to perform two video-related operations:
 Load a Run-Out video
 check whether it is able to run smoothly
2. Split video module
 Divide the video into frame
 It is required because traditional image processing techniques
are applied on still images
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9. 3. Gray scale converter
 Detect crease and the wicket within a frame.
 Perform a pre-processing technique called gray scaling.
 Convert video into a digital signal in order to effectively apply
Image processing techniques.
 That is a frame is converted into a discrete numbers of shades
of gray.
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10. 10
GUI1:A loaded video divided into frame

11. MOTION DETECTION ALGORITHM
 It is based on a simple comparison of the pixels across
consecutive frames.
 A set of pixels are different from the same set of pixels in
consecutive frame ,is the frame difference
 Set frame difference threshold to 0.1
 Once the motion regions in a frame is identified, use a
technique known as blob counting
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Five objects detected in a relevant frame.
•This allows to determine the amount of detected objects ,
the position and size of each detected object

13.  MDA detects insignificant objects that are not relevant for Run-
Out detection.
 MDA is never able to detect the crease.
 In GUI 2 there are two identification markers
 Crease marker
 Wicket marker
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14. 14
Wicket and crease markers on a loaded frame.

15. 4. Object tuner module
 User can tune the position of the crease and wicket markers
5. Object detector module
 Detect objects whose motion occur around crease and wicket
markers.
6. Pixel capture module
 Captures all the pixels related to the two markers.
 For each frame , it captures the 50 pixels that comprise the
wicket marker. 15

16. •For the crease marker , it uses three pre-defined rectangles of
equal size, where each rectangle comprises 600 pixels.
Capturing pixels on the wicket marker & crease marker 16

17. 7. Decision detector module
 Detects a Run-Out or a Not-Out by comparing the content of
the pixels.
 If WicketChange = true, CreaseChange = false- ‘Run-Out’
 WicketChange = false, CreaseChange = true- ‘Not-Out’
 WicketChange = true, CreaseChange = true- ‘Not-Out’
 WicketChange = false, CreaseChange = false- ‘Not-Out’
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18. 8. Umpire rater module
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Scenario for assigning rating to field umpires; A = bat detection, B = ball
detection, C = difference in frames

19.  A-Eye can be used to calculate a rating for the performance of
the field umpires.
 C <= 5:Detecting A and B is quite tough for the field umpire. If
he is still able to give the correct Run-Out decision then
ratingUp.
 C > 5:Enough frames have elapsed in order to allow the field
umpire to make the Run-Out decision. if he still refers the
decision to the A-Eye then ratingDown.
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20. APPLICATION
 Automating the run-out decision
 Rating the field umpire
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21. CONCLUSION
 It is able to decide autonomously whether a batsman is out or
Not-Out in a Run-Out situation.
 A-Eye is extremely efficient as compared to the third umpire
 Accuracy of A-Eye are very similar to that of third umpire.
 A-Eye consume considerably less time as compared to third
umpire.
 Minimize the element of human error.
 It can estimate a rating for the performance of the field umpires
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22. FUTURE ENHANCEMENT
 In the future, we can use A-Eye in 3D environment
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23. REFERENCE
 Gonzalez, R. C., & Woods, R. E., (2002). Digital image
processing (2nd ed.), PrenticeHall.
 Han, J. (2005). Data mining: concepts and techniques. San
Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
 Jahne, B., & Haussecker, H. (2000). Computer vision and
applications: a guide for students and practitioners. Academic
Press.
 Nielsen, J. (199). Usability engineering. Academic Press
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24. Any Queries
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