3 research outputs found
Play type recognition in real-world football video
This paper presents a vision system for recognizing the sequence of plays in amateur videos of American football games (e.g. offense, defense, kickoff, punt, etc). The sys-tem is aimed at reducing user effort in annotating foot-ball videos, which are posted on a web service used by over 13,000 high school, college, and professional football teams. Recognizing football plays is particularly challeng-ing in the context of such a web service, due to the huge variations across videos, in terms of camera viewpoint, mo-tion, distance from the field, as well as amateur camerawork quality, and lighting conditions, among other factors. Given a sequence of videos, where each shows a particular play of a football game, we first run noisy play-level detectors on every video. Then, we integrate responses of the play-level detectors with global game-level reasoning which accounts for statistical knowledge about football games. Our empir-ical results on more than 1450 videos from 10 diverse foot-ball games show that our approach is quite effective, and close to being usable in a real-world setting. 1
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Offensive Direction Inference in Real-World Football Video
Automatic analysis of American football videos can help teams develop strategies and extract patterns with less human effort. In this work, we focus on the problem of automatically determining which team is on offense/defense, which is an important subproblem for higher-level analysis. While seemingly mundane, this problem is quite challenging when the source of football video is relatively unconstrained, which is the situation we face. Our football videos are collected from a web-service used by more than 13, 000 high school, college, and professional football teams. The videos display huge variation in camera viewpoint, lighting conditions, football field properties, camera work, among many other factors. These factors taken together make standard off-the-shelf computer vision algorithms ineffective. The main contribution of this thesis is to design and evaluate two novel approaches for the offense/defense classification problem from raw video, which make minimal assumptions about the video properties. Our empirical evaluation on approximately 1200 videos of football plays from 10 diverse games validate the effectiveness of the approaches and highlight their differences
A KD framework in football data analytics: a value co-creation framework for the use of knowledge discovery technologies in the football industry
Investment in sport technologies are expected to grow by 40.1% during 2016-2022 reaching approximately $3.97 billion by 2022. As well the recent changes in technology regulations by The Federation Internationale de Football Association (FIFA) since the 2018 World Cup created promising football technologies. This research questions addressing the issue of what is the value of such technologies for professional football teams? and what are the benefits of these technologies? This is achieved by developing a framework for understanding the value co-creation process from the knowledge discovery systems in the football industry. The framework aids in mapping the resources, pinpointing the outputs, identifying the competencies leading into capabilities, and finally in realisation of the value of the final outcomes in that journey. On another words, different teams have different resources that allow them to achieve certain outputs. These outputs enable the coaching team to achieve and maintain certain abilities. By changes in practice the will improve the team ability and enhance their analytical capabilities. Therefore, that will allow and aid the coaching team to gain new outcomes such as improving training strategies, transferring players, and informative match strategies. Additionally, improved understanding of the value co-creation process from the knowledge discovery systems in the football industry answering, why are some teams better able to gain value from investment in knowledge discovery technologies than other teams in the football industry. The framework has been developed in three phases in which semi-structured interviews where used in the first and second phases for developing and validating the framework respectively. The third and final phases is verifying the framework by developing a knowledge discovery maturity model as an online assessment s tool in operationalising the research findings. The main contributions of this research are the adaptation and customisation of Melville et al. (2004) to develop a value co-creation process form knowledge discovery resources. Moreover, applying Agile (APM, 2015) artefacts and techniques and tools in improving the value co-creation process between coaches and data analysts. That s aided in developing the value co-creation knowledge discovery framework in football analytics. Additionally, the development of a key performance indicators balanced scorecard and its adaptation as a in understanding the relationships between the key performance indicators (i.e. physical, psychological, technical and tactical performance indicators). Finally, the development of the knowledge discovery maturity model in football analytics which was used in understanding and pinpointing areas of strength and weakness in the utilisation of the various football resources used in football analytics (human resources, technological resources, value co-creation resources and analytical models used)