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Real Time Automated Analysis of Soccer Broadcast VideosAuthor: Yaparla Ganesh Date: 2020-04-18 Report no: IIIT/TH/2020/32 Advisor:Garimella Ramamurthy AbstractSports data is probably the most widely looked at and analysed data source in the world. Data captured during a sports match is of much interest to fans who use data to validate their hypotheses about player/team performances and to conduct an analysis of the happenings during the game. Data collection has largely been a manual process and thus the data was discrete in nature and outcome based. For example in the case of cricket, for a ball bowled the outcome was recorded as to how many runs were scored but the position the ball was pitched and where it passed the batsman can not be recorded with a reasonable level of consistency, if done manually. With the introduction of various technology based tracking solutions, such kinds of spatial data that is consistent, can now be complied. When you think of continuous sports such as football, hockey this kind of data is continuous and holds much more context than that the discrete, outcome based data used to present. The 2 methods used currently are either tracking through hardware tracking devices that need to be attached to each object of interest or by using computer vision techniques to analyze videos of the game that must be recorded by multiple specially installed single purpose cameras. Hence, as the data made available is becoming richer we see that sports players and teams are increasingly making use of data to devise strategies and to gain a competitive advantage. This is making such data very powerful as sports teams can use it for prescribing tactics, betting companies for better predictions, and sport broadcasters and fans for analysis. Spatial data is especially interesting in football because the dynamic positions of the players on the playing field are highly important to decide the outcome of the game. Football players are often advised by their managers precisely as to how they must position themselves during different passages of play. Managers are often thought to be playing chess with each other as they plan their moves and organize their troops in various tactical formations aimed at giving them a leg-up over their opponent. The importance of spatial data in football cannot be overstated. We have taken a different approach of using computer vision to gather data pertaining to player and ball location by working purely off a broadcast video for the sport of football. The benefits of this approach are that it is economically cheaper and can also be used to analyze any televised match from the past. The 2 other techniques discussed are highly intrusive and expensive to setup. Another drawback with these techniques is that they need specially installed hardware prior to the start of the game to gather data. Using our technique we can analyze data of any televised football game easily and also use it to analyze any televised game for the past. As data hungry machine learning models are being increasingly used to prescribe and predict, opening up the data from all the games in the past could be hugely significant and we hope our research proves a significant step in that direction Full thesis: pdf Centre for Others |
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