A unified and efficient framework for court-net sports video analysis using 3D camera modeling

The extensive amount of video data stored on available media (hard and optical disks) necessitates video content analysis, which is a cornerstone for different user-friendly applications, such as, smart video retrieval and intelligent video summarization. This paper aims at finding a unified and efficient framework for court-net sports video analysis. We concentrate on techniques that are generally applicable for more than one sports type to come to a unified approach. To this end, our framework employs the concept of multi-level analysis, where a novel 3-D camera modeling is utilized to bridge the gap between the object-level and the scene-level analysis. The new 3-D camera modeling is based on collecting features points from two planes, which are perpendicular to each other, so that a true 3-D reference is obtained. Another important contribution is a new tracking algorithm for the objects (i.e. players). The algorithm can track up to four players simultaneously. The complete system contributes to summarization by various forms of information, of which the most important are the moving trajectory and real-speed of each player, as well as 3-D height information of objects and the semantic event segments in a game. We illustrate the performance of the proposed system by evaluating it for a variety of court-net sports videos containing badminton, tennis and volleyball, and we show that the feature detection performance is above 92% and events detection about 90%.