Automatic Analysis of Cricket And Soccer Broadcast Videos

Rahul Anand Sharma (homepage)


In the past recent years, there has been a growing need to understand the semantics in sports games. Use of technology in analyzing player movements and understanding the action on a sports field has been growing in the past few years. Most of the systems today make use of certain tracking devices worn by players or markers with sensors placed around the play area. These trackers or markers are electronic devices that communicate with the cameras or cameramen. Other technologies such as the goal line technology popularly used in soccer helps game referees to make accurate decisions that are often misjudged by mere human perception. The primary challenges in these techniques is to make it cost effective and ease of installation and use. It is not convenient to setup markers and sensors around the playing field or to force players to wear certain recording or communication devices without affecting their natural style of playing. Placing a sensor in the game ball also poses a tricky problem of not altering the physical properties of the ball. Sports recorders and broadcasters are now looking for simple and yet effective solutions to get semantic information from a sports game. The big question here is - Can we get sufficient important data only from a video capture just as a human would without relying on external aids of markers and sensors? With advances in various computer vision algorithms and techniques the goal for the future is to analyze everything from captured video. This kind of solution is obviously more attractive to broadcasting and game recording companies as they dont need to setup extra equipment, or influence the authorities to change the match ball or players outfits. We propose a set of algorithms that does the task of automatic analysis for broadcast videos for the sports of Cricket and Soccer.. Using our approach we can automatically detect salient events in Soccer, Temporally align Cricket video with corresponding text commentaries, Localize/Register a soccer image and others. We also compare our algorithms with other state of the art approaches extensively on different datasets for a variety of tasks.


Year of completion:  December 2016
 Advisor : Prof. C.V. Jawahar

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