How to Give Cricket Commentary,Teaching Computers
In the future, will journalists be put out of their jobs by robots? It’s a distinct possibility, starting with live coverage of sporting events. In a research paper published late last month, Rahul Anand Sharma, and CV Jawahar, scientists at IIIT Hyderabad, along with Pramod Sankar K, of Xerox Research Center India, explain how they used machine learning techniques to generate text-based cricket commentary, with an accuracy rate of 90 percent.
“In the first stage, the video is segmented into ‘scenes’, by utilising the scene category information extracted from text commentary. The second stage consists of classifying videoshots as well as the phrases in the textual description into various categories. The relevant phrases are then suitably mapped to the video-shots,” says an excerpt from the paper titled “Fine-Grain Annotation of Cricket Videos”.
Gadgets 360 spoke to Professor CV Jawahar about the paper, and he explained that the solution could be used by sports websites to automate and assist reporters in writing real-time cricket commentary.
“We use readily available data like broadcast videos and Cricinfo commentary as examples for these machine learning methods,” says Jawahar. “To learn such a representation, several examples are needed. A computer program then learns from these examples using machine learning algorithms, and tags parts of the video with these labels.”
The video dataset was collected from the YouTube channel for the Indian Premier League (IPL) tournament, while snippets of commentary were scraped by crawling through commentary for about 300 matches on Cricinfo. The scientists’ computer algorithms were able to accurately label a batsman’s cricketing shot by using visual-recognition techniques on an action that last a mere 35 frames, or 1.2 seconds.
Exploring the potential innovations or applications that could result from the research, Jawahar said that beyond automated commentary generation, their work also enables enthusiasts and experts to study the game deeply, and search for a specific aspect of the game. According to the paper, the annotation of the videos allows the researchers to build a retrieval system that can search across hundreds of hours of content for specific actions that last only a few seconds. Cricket teams could use the annotation technology to analyse strengths and weaknesses of a particular player – batting strokes that are effective against a particular team or bowler, or to study the kinds of deliveries a batsman is weak against.
“For example, one could learn how Rahul Dravid modified his straight drive over years, from automatically annotated video databases,” says Jawahar. “It can also help in game strategy planning for the team management. This can also help in training or coaching emerging players.”
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