Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest.
We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating
their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score (mAP@0.5), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player’s reaction time, speed, and footwork around the court, etc.

 @inproceedings{ghosh2018towards,
    title={Towards Structured Analysis of Broadcast Badminton Videos},
    author={Ghosh, Anurag and Singh, Suriya and Jawahar, C.~V.},
    booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV 2018), Lake Tahoe, CA, USA},
    pages={9},
    year={2018}
}
 

Dataset

The annotations can be downloaded from here: Dataset

The annotations are provided as EAF Files, which can be opened in ELAN annotation tool (available at https://tla.mpi.nl/tools/tla-tools/elan/download/). The links to the youtube videos are provided in supplementary (will be required to view the annotations with the video).
We will be releasing the features soon!