Automatic data volley: game data acquisition with temporal-spatial filters

Abstract

AbstractData Volley is one of the most widely used sports analysis software for professional volleyball statistics analysis. To develop the automatic data volley system, the vision-based game data acquisition is a key technology, which includes the 3D multiple objects tracking, event detection and quality evaluation. This paper combines temporal and spatial features of the game information to achieve the game data acquisition. First, the time-vary fission filter is proposed to generate the prior state distribution for tracker initialization. By using the temporal continuity of image features, the variance of team state distribution can be approximated so that the initial state of each player can be filtered out. Second, the team formation mapping with sequential motion feature is proposed to deal with the detection of event type, which represents the players’ distribution from the spatial concept and the temporal relationship. At last, to estimate the quality, the relative spatial filters are proposed by extracting and describing additional features of the subsequent condition in different situations. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Mens Volleyball in Tokyo Metropolitan Gymnasium. The results show 94.1% rounds are successfully initialized, the event type detection result achieves the average accuracy of 98.72%, and the success rate of the events’ quality evaluation achieves 97.27% on average.