Data Volley 2007 Crack
Janet Denzel
Get the analysis of what is happening with competitors and within your volleyball leagues. Keep track of the rally and type the codes to check the statistics. Monitor every contact in real time during a point and perform the analysis of different perspectives of the game.
With the high-speed development of the vision sensing devices, the vision-based sports analysis technologies contribute in more and more fields, such as TV broadcasting contents, strategy development and coaching system. In game broadcasting, precise and real-time game data will provide much more attractive contents to the audiences to understand the game status. In the strategy development and coaching system, abundant game data help the teams and players to know their strengths and weaknesses, so that the proper strategy and personal training plans can be developed efficiently.
The performance of the sports analytic tools and the reliability of the game strategy development are depending on the quality and quantity of the game data. Therefore, the big data of the network are expected to support the sports analysis and other applications. [1, 2] Some researches pay attentions to the applications of sports analysis based on big data [3, 4], which will undergo major changes thanks to the utilization of the big data. However, the social network for utilizing big data is mature while how to obtain the data becomes key problem in real applications.
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 [5] 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.
Based on the statement above, the research target of this article is the vision-based game data acquisition for the automatic Data Volley system. With the game videos, the computer vision technology will provide more accurate data than manually input, especially for the position and velocity information, so that the reliability and accuracy of the game data analysis will increase. According to the data being used in the Data Volley software and considering other game data which are useful for the analysis of game strategy, the requirement of automatic data acquisition from game video are stated as following. Firstly, the available game playing period should be detected from the entire game video, which also includes the rest time, pause time and other parts besides playing. Secondly, all the players should be distinguished from each other, and at each event, the locations of players are required. Thirdly, as the evaluation criteria required, the trajectory including the position and velocity of the ball is also important data. Fourthly, the game events are required to present the game status. At last, based on the information of the ball and players in one event, the evaluation of the event is required.
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.
There have been several researches targeting on the development of the automatic game analysis system. Work [7] proposes a computationally efficient hybrid method for automatic sports highlights generation to make contributions for the broadcasting applications. Method [8, 9] proposes a trajectory and action recognition of the player to analyze soccer training videos. This work has strict limitations on the environment and it only can be used in single player training scenario. Work [10] predicts the team events by analysis of the player motions and performance in basketball and water polo. This work analyzes the data by transferring the input video to overhead view, so that only 2D team sports can be used. Work [11] estimates the team tactics in soccer game videos based on the deep learning method and unique characteristics of tactics. Work [12] presents techniques for automatically classifying players and tracking ball movements in game video clips to analyze basketball movements and pass relationships.
In addition, most above multiple players tracking algorithms are initialized manually. The research targeting on automatic initialization of tracking [25, 26] are based on object detection results. These detection based methods are weak at the occlusion and similar appearance problems, which often occur in volleyball game.
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 [44] 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.
The automatic Data Volley system consists of two parts: the automatic game data acquisition and the data analysis/strategy development. The overall framework and tasks of the automatic game data acquisition is marked with rectangle, in which the proposals are denoted with red color
First, the preprocessing consists of the multi-video synchronization, camera calibration, and the play scene detection. The video synchronization is for aligning different views to fully utilize the image information. The camera calibration is the key to create the projection relation between each image to the real physical world. With the input videos, the play scene detection method outputs the time at which one round begins and the subsequent data acquisition process starts.
Second, the basic data acquisition consists of the physical data tracking and the event detection. For the physical data tracking, 3D ball tracking [32] and multiple players tracking [45] are implemented to obtain the 3D trajectories of the ball and players. Here, we proposed a time-vary fission filter to approximate the initial distribution of the whole team to automatically initialize the tracker.
By connecting combining the output of all the steps, the required data of automatic Data Volley are collected and the following processing of data analysis and strategy development can be applied. The detail algorithm of each proposal is described in the following sections.
First, with the target of automatic tracker initialization, the distribution of each player is fissured from the team state distribution. The image feature is extracted to calculate the confidence value of each player and the temporal feature of trajectory is utilized to distinguish the players from each other. Second, the event detection is achieved through combination of the sequential motion and the team formation mapping. The sequential motion refers the order of the event specified by the game rules. The team formation mapping describes the event feature from the perspective of the whole court space, not just looking at the state of individual player. Third, the temporal and spatial features are extracted respectively for different evaluation criteria. The event series feature refers the subsequent events, which representing the consequence of the event, while the relative spatial filter utilizes the additional 3D location information. Therefore, the overall automatic data acquisition system combines the advantage of both the temporal and spatial information.
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