Sai Siddartha Maram
ABSTRACT
Cricket is a game that requires players to constantly adapt to situations and customize their game depending on opponents and playing conditions. Players and coaching staff often watch video clips to understand the strategies of opponents. Iterating through multiple matches over many years across various leagues and formats, and extracting clips is a tiring process. In this paper, we propose a computer vision framework to segment cricket matches into clips based on context and construct real-time graphs using meta-data from segmented clips. We discuss various queries on the generated graphs and also evaluate our segmentation and querying model based on the accuracy and quality of the retrieved data.
- N. Babaguchi, Y. Kawai, T. Ogura, and T. Kitahashi. 2004. Personalized abstraction of broadcasted American football video by highlight selection. IEEE Transactions on Multimedia , Vol. 6, 4 (2004), 575--586.Google Scholar
Digital Library
- A. Baijal, Jaeyoun Cho, Woojung Lee, and Byeong-Seob Ko. 2015. Sports highlights generation bas ed on acoustic events detection: A rugby case study. In 2015 IEEE International Conference on Consumer Electronics (ICCE) . 20--23.Google ScholarCross Ref
- K. Chaudhary, M. Gupta, and P. kaur. 2019. Analyzing IPL dataset with MongoDB. In 2019 9th International Conference on Cloud Computing, Data Science Engineering (Confluence) . 212--216.Google ScholarCross Ref
- B. Ghanem, M. Kreidieh, M. Farra, and T. Zhang. 2012. Context-aware learning for automatic sports highlight recognition. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) . 1977--1980.Google Scholar
- Hao Tang, V. Kwatra , M. E. Sargin, and U. Gargi. 2011. Detecting highlights in sports videos: Cricket as a test case. In 2011 IEEE International Conference on Multimedia and Expo. 1--6.Google Scholar
- A. Javed, K. B. Bajwa, H. Malik, and A. Irtaza. 2016. An Efficient Framework for Automatic Highlights Generation from Sports Videos. IEEE Signal Processing Letters , Vol. 23, 7 (2016), 954--958.Google ScholarCross Ref
- Maheshkumar H. Kolekar, Student Member, and Somnath Sengupta. 2006. Event-importance Based Customized and Automatic Cricket Highlight Generation. In in IEEE Int. Conf. on Multimedia and Expo. 1617--1620.Google Scholar
- B. Li and M. Ibrahim Sezan. 2001. Event detection and summarization in sports video. In Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL 2001) . 132--138.Google Scholar
- Kamesh Namuduri. 2009. Automatic extraction of highlights from a cricket video using MPEG-7 descriptors. 1st International Conference on Communication Systems and Networks and Workshops, COMSNETS 2009, 1 -- 3. https://doi.org/10.1109/COMSNETS.2009.4808848Google ScholarCross Ref
- D. Seo, S. Kim , H. Park, and H. Ko. 2014. User generated highlight system for baseball games with social media activities. In 2014 IEEE International Conference on Consumer Electronics (ICCE) . 349--350.Google Scholar
- R. A. Sharma, B. Bhat, V. Gandhi, and C. V. Jawahar. 2018. Automated Top View Registration of Broadcast Football Videos. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). 305--313.Google Scholar
- H. Shih. 2018. A Survey of Content-Aware Video Analysis for Sports. IEEE Transactions on Circuits and Systems for Video Technology , Vol. 28, 5 (2018), 1212--1231.Google ScholarCross Ref
- Pushkar Shukla, Hemant Sadana, Apaar Bansal, Deepak Verma, Carlos Elmadjian, Balasubramanian Raman, and Matthew Turk. 2018. Automatic Cricket Highlight Generation Using Event-Driven and Excitement-Based Features. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops .Google Scholar
- A. Tejero-de-Pablos, Y. Nakashima, T. Sato, N. Yokoya , M. Linna, and E. Rahtu. 2018. Summarization of User-Generated Sports Video by Using Deep Action Recognition Features. IEEE Transactions on Multimedia , Vol. 20, 8 (2018), 2000--2011.Google ScholarCross Ref
- Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , Vol. 13, 4 (2004), 600--612.Google ScholarDigital Library
Index Terms
- Asking Graphs "How Did I Play?" Generating Graphs through Images Via Signals
Recommendations
Anomaly Detection in Player Performances in Multiplayer Online Battle Arena Games
ACSW '22: Proceedings of the 2022 Australasian Computer Science WeekEsports are digital video games that are played professionally. In recent years there has been a growing need to improve the broadcast experience by incorporating real-time data-driven analytics. In these same games, when played by the general public, ...
Towards understanding how to design for social play in exertion games
Players invest significant physical effort when playing exertion games. In addition to improving physical health, exertion games are also believed to facilitate social play amongst players. Despite these advantages, our understanding of how to design ...
Insights of the augmented dodgeball game design and play test
AH '17: Proceedings of the 8th Augmented Human International ConferenceModern technology enables us to create novel devices and provide exciting new user experiences. In today's world where few people get engaged to physical activities regularly and in the amounts recommended by the doctors, it is important to find ways to ...
Comments