For the data acquisition in real game, only the 3D physical data (especially the position and speed information) make sense in data analysis and strategy development. However, most existing practical products for data acquisition only extract and present the game data (such as the position information) in the image coordinate system. From this point of view, volleyball is a typical object of 3D sports analysis, since both the ball and the players require the 3D concept to describe their motions. Once the volleyball game data can be acquired by computer vision method, other game data can also be obtained in similar way.
For the data acquisition and analysis of volleyball game, Data Volley  is the most widely used software for professional statistics analysis of volleyball games. This software can not only record the technical and tactical playing data of the players from both teams by a convenient interface, but also get a variety of statistical analysis data immediately in the statistical process, which helps the coaches to conduct real-time analysis and on-the-spot guidance for the game. In Data Volley, all input data are observed and judged by peoples through watching the game. This input process not only costs large human labor and time but also lacks of data accuracy since human eyes are weak at measuring the distance, velocity and time. Therefore, this article targets on the automatic and precise game data acquisition method to development the automatic Data Volley system with high reliability and efficiency of the volleyball game analysis.
In this paper, we propose data acquisition methods to automatically collect the game data required by the Data Volley software from complete volleyball games. Our contributions are summarized as follows:
In this section, the related works are discussed for different tasks. The target of this article is automatic Data Volley for the volleyball game analysis. With the similar target, the vision-based game analysis methods are discussed at first. Then the data acquisition for automatic Data Volley can be divided into several tasks, the detection of play scene, the multiple player tracking, the ball tracking, the event detection and event evaluation. For each task, we use one subsection to discuss the related work. Among these tasks, the detection of play scene and the ball tracking have been achieved by the conventional works. And the left ones are the main works in this article.
In order to analyze the quality or evaluate the performance of the sports, some researches [42, 43] focus on the method of statistical analysis. By accessing historical data, these works analyze different factors of the overall games and draw up evaluation report based on certain criteria. These researches focus on the performance of the whole team and do not pay much attentions on a certain action or event. To obtain quality information of the receive event, we proposed a framework  for qualitative action recognition for volleyball game analysis. This work evaluates the quality based on the return ball quality and the posture quality, which is different to the definition of event evaluation in Data Volley. In general, there is few research targeting on the event quality evaluation. Since the event quality is defined according to specific game rule, the very few existing method cannot be used as a comparison.
The multiple cameras are used to record the game from different view-angles so that we can obtain multi-view videos as the input. The reason multi-view videos are used in this system is because it is difficult to construct precise 3D coordinate from single view information. Although there are some works [29, 31] estimating the 3D coordinate only using single video, it requires heavy algorithms to compensate the reconstruction error. In addition, the multi-view videos are robust for occlusion situation. In volleyball game, there are always twelve players in the court who share same appearances and are overlapped by each other. In order to ensure a high data precision, multiple cameras are used to reduce the difficulties of the occlusion problem.
For each round, the trajectory information are acquired by the tracker initialization, ball tracking , players tracking  works. Then, among the correct tracking rounds, we labeled the event type manually. The overview of the data set is summarized in Table 3. The totally number of available event Serve, Receive, Set, Attack, Block, Dig and Free Ball are 24, 36, 100, 118, 47, 82, and 8. One thing to be mentioned is that the data set includes little numbers of the event Free Ball, so it is hard to conduct experiments. Besides, as it is discussed in previous chapters, this kind of event is not typical in general volleyball analysis. So the experiment does not take this event into consideration either.
Our future target will dive into two aspects. First, combining current work with the game strategy knowledge and rules, a comprehensive automatic Data Volley system consisting of game data acquisition and tactics development is our future topic. Furthermore, based on the achievements of the GPU real-time acceleration [45, 49], an real-time and low-delay automatic data volley analysis system is expected for the supporting of TV content broadcasting in international big events such as Olympic Games.
The APR compilations, first made public in 2005, are based on academic eligibility, retention and graduation rates for student-athletes at all 334 NCAA Division I member institutions. These scores and multiyear rate figures reflect the data from the 2007-08, 2008-09, 2009-10 and 2010-11 academic years. Samford competes in eight men's and nine women's varsity sports at the Division I level. In the inaugural overall ratings, involving the 2003-04 school year, Samford tied for seventh nationally. 2b1af7f3a8