A-Eye: Automating the role of third umpire in the game of cricket

1. A-EYE: AUTOMATING THEROLE OF THE THIRD UMPIREIN THE GAME OF CRICKETPresented byAneesh.T.GRoll no:6S7 IT

2. ABSTRACT In cricket ,currently for giving umpiring decisions likestumping and run out ,the third umpire has to review variousangular video footage This process consume around one minute which disrupts thepace of the game In A-Eye a set of autonomously filmed run-out videos areapplied 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 processingtechniques Illustrate the working of various architectural components ofA-Eye and algorithm for automating the Run-Out decision. Conclusions along with the future work.3

4. EXISTING SYSTEM4 Currently third umpiring is used.DisadvantageDisadvantage While the third umpire is making his decision, all the playershave 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.

6. PROPOSED SYSTEM6 A-Eye: Automating the role of the third umpire.Advantage Robust Minimize the decision time

7. SYSTEM ARCHITECTURE GUI 1 is initially used to loadand perform some pre-processingtasks GUI 2 is then used to detect themotion at the wicket and thecrease7Architecture 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 smoothly2. Split video module Divide the video into frame It is required because traditional image processing techniquesare applied on still images8

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 applyImage processing techniques. That is a frame is converted into a discrete numbers of shadesof gray.9

10. 10GUI1:A loaded video divided into frame

11. MOTION DETECTION ALGORITHM It is based on a simple comparison of the pixels acrossconsecutive frames. A set of pixels are different from the same set of pixels inconsecutive frame ,is the frame difference Set frame difference threshold to 0.1 Once the motion regions in a frame is identified, use atechnique known as blob counting11

12. 12Five 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 marker13

14. 14Wicket and crease markers on a loaded frame.

15. 4. Object tuner module User can tune the position of the crease and wicket markers5. Object detector module Detect objects whose motion occur around crease and wicketmarkers.6. Pixel capture module Captures all the pixels related to the two markers. For each frame , it captures the 50 pixels that comprise thewicket marker. 15

16. For the crease marker , it uses three pre-defined rectangles ofequal 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 ofthe 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-Out17

18. 8. Umpire rater module18Scenario for assigning rating to field umpires; A = bat detection, B = balldetection, C = difference in frames

19. A-Eye can be used to calculate a rating for the performance ofthe field umpires. C 5:Enough frames have elapsed in order to allow the fieldumpire to make the Run-Out decision. if he still refers thedecision to the A-Eye then ratingDown.19

20. APPLICATION Automating the run-out decision Rating the field umpire20

21. CONCLUSION It is able to decide autonomously whether a batsman is out orNot-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 thirdumpire. Minimize the element of human error. It can estimate a rating for the performance of the field umpires21

22. FUTURE ENHANCEMENT In the future, we can use A-Eye in 3D environment22

23. REFERENCE Gonzalez, R. C., & Woods, R. E., (2002). Digital imageprocessing (2nd ed.), PrenticeHall. Han, J. (2005). Data mining: concepts and techniques. SanFrancisco, CA, USA: Morgan Kaufmann Publishers Inc. Jahne, B., & Haussecker, H. (2000). Computer vision andapplications: a guide for students and practitioners. AcademicPress. Nielsen, J. (199). Usability engineering. Academic Press23

24. Any Queries24

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A-Eye: Automating the role of third umpire in the game of cricket

Presentations & Public Speaking

loaded frame

relevant frame

loaded video

process video module

split video module

video check

crease markers

frame difference threshold