Automatic detection of highlights from a Cricket Match
1 like472 views
The document discusses the development of a machine learning and computer vision system by the Indian Institute of Science to analyze cricket matches by automatically detecting runs and wickets in real-time video streams. It outlines system capabilities, including live streaming of highlights, accuracy rates, and potential improvements, while indicating the need for a semi-automated approach to enhance performance. The research scope also suggests possible expansions into other sports and further advancements in action recognition and player tracking.
Automatic detection of highlights from a Cricket Match
- 1. Outline The Story of 641.com Machine Learning Lab1 Computer Vision Lab2 1 Department of Computer Science Indian Institiue of Science 2 Department of Electrical Engineering Indian Institiue of Science 31-July-2008 ML lab & CV Lab The Story of 641.com
- 2. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 3. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 4. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 5. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 6. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 7. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 8. Outline Outline 1 Runs, Runs everywhere 2 Where is the problem? 3 So What? 4 How to do that? 5 Ok.. How good is it? 6 What now? 7 Lets see it ! ML lab & CV Lab The Story of 641.com
- 9. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Runs, Runs everywhere Cricket is all about ‘Runs’ & ‘Wickets’ 30 Years of Runs and Wickets in archives Atleast 50 International Matches per player per year On the average 4-5 fours+sixes+wickets per match per player 12-14 players per international team Over 10 international teams So what Over 10,50,000 interest points! ML lab & CV Lab The Story of 641.com
- 10. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Can we extract & classify 6,4 & W automatically In Live video streams? Can we do it in real time? How accurately can we do it? In Archived videos? How good is the summary? How much ‘valid’ data is lost? How much compression is obtained? Retrieval speed, Preprocessing etc.. ML lab & CV Lab The Story of 641.com
- 11. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Implications 1 Automated highlights in real time. 2 Live streaming of highlights to Mobiles (even in low bandwidth environments) 3 Interactive search platform for yesteryears great performances ML lab & CV Lab The Story of 641.com
- 12. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Implications 1 Automated highlights in real time. 2 Live streaming of highlights to Mobiles (even in low bandwidth environments) 3 Interactive search platform for yesteryears great performances ML lab & CV Lab The Story of 641.com
- 13. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Implications 1 Automated highlights in real time. 2 Live streaming of highlights to Mobiles (even in low bandwidth environments) 3 Interactive search platform for yesteryears great performances ML lab & CV Lab The Story of 641.com
- 14. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Implications 1 Automated highlights in real time. 2 Live streaming of highlights to Mobiles (even in low bandwidth environments) 3 Interactive search platform for yesteryears great performances ML lab & CV Lab The Story of 641.com
- 15. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! How to do that? ML lab & CV Lab The Story of 641.com
- 16. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! How to do that?(contd...) ML lab & CV Lab The Story of 641.com
- 17. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! How to do that?(contd...) ML lab & CV Lab The Story of 641.com
- 18. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Ok.. How good is it? System Specs.. AMD turion 1.7Ghz, 512 MB RAM , Matlab 7.3 (8x1.25hr videos) 1 Accuracy Avg. Misclassifaction rate : 5% (May miss some score changes) 2 How long will it take !! Complete run (2 hr video ) : 25 min Human intervention? Completely Automated!!! ML lab & CV Lab The Story of 641.com
- 19. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Ok.. How good is it? System Specs.. AMD turion 1.7Ghz, 512 MB RAM , Matlab 7.3 (8x1.25hr videos) 1 Accuracy Avg. Misclassifaction rate : 5% (May miss some score changes) 2 How long will it take !! Complete run (2 hr video ) : 25 min Human intervention? Completely Automated!!! ML lab & CV Lab The Story of 641.com
- 20. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Ok.. How good is it? System Specs.. AMD turion 1.7Ghz, 512 MB RAM , Matlab 7.3 (8x1.25hr videos) 1 Accuracy Avg. Misclassifaction rate : 5% (May miss some score changes) 2 How long will it take !! Complete run (2 hr video ) : 25 min Human intervention? Completely Automated!!! ML lab & CV Lab The Story of 641.com
- 21. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Ok.. How good is it? System Specs.. AMD turion 1.7Ghz, 512 MB RAM , Matlab 7.3 (8x1.25hr videos) 1 Accuracy Avg. Misclassifaction rate : 5% (May miss some score changes) 2 How long will it take !! Complete run (2 hr video ) : 25 min Human intervention? Completely Automated!!! ML lab & CV Lab The Story of 641.com
- 22. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Ok.. How good is it? System Specs.. AMD turion 1.7Ghz, 512 MB RAM , Matlab 7.3 (8x1.25hr videos) 1 Accuracy Avg. Misclassifaction rate : 5% (May miss some score changes) 2 How long will it take !! Complete run (2 hr video ) : 25 min Human intervention? Completely Automated!!! ML lab & CV Lab The Story of 641.com
- 23. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! What now? 1 Live Mobile streaming of highlights. 2 From TV broadcast to Mobile streaming : under 2 min. 3 Easy to archive and retrieve old videos. 4 can be used for pictorial summarization. Lighter Note Seems like a good startup idea!! ML lab & CV Lab The Story of 641.com
- 24. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Research Scope 1 Action Recognition (like hooks ,pulls etc.,). 2 Player detection and tracking . Generic Can we make some of these techniques work for other sports like football. ML lab & CV Lab The Story of 641.com
- 25. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Research Scope 1 Action Recognition (like hooks ,pulls etc.,). 2 Player detection and tracking . Generic Can we make some of these techniques work for other sports like football. ML lab & CV Lab The Story of 641.com
- 26. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Research Scope 1 Action Recognition (like hooks ,pulls etc.,). 2 Player detection and tracking . Generic Can we make some of these techniques work for other sports like football. ML lab & CV Lab The Story of 641.com
- 27. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Lets see it ! 1 Streaming through web site. 2 Streaming to mobile phones. 3 Real time streaming of live broadcast to mobile phones. ML lab & CV Lab The Story of 641.com
- 28. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Lets see it ! 1 Streaming through web site. 2 Streaming to mobile phones. 3 Real time streaming of live broadcast to mobile phones. ML lab & CV Lab The Story of 641.com
- 29. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Lets see it ! 1 Streaming through web site. 2 Streaming to mobile phones. 3 Real time streaming of live broadcast to mobile phones. ML lab & CV Lab The Story of 641.com
- 30. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! More Statistics? ML lab & CV Lab The Story of 641.com
- 31. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Semi Automated Approach ML lab & CV Lab The Story of 641.com
- 32. Runs, Runs everywhere Where is the problem? So What? How? How good is it? What now? Lets see it ! Semi Automated Approach - Statistics Even though the Completely automated algorithm has real good results, it misses some shots. Reason: The scorecard boundary is not perfect. Adhoc Solution: Semi Automated approach in which the scorecard boundary is manually input by the user. About 10% improvement over and above the preformance of the CA algorithm is obtained. ML lab & CV Lab The Story of 641.com