A novel multimedia data mining framework for information extraction of a soccer video stream

A video stream is usually massive in terms of data content with abundant information. In the past, extracting explicit semantic information from a video stream; i.e. object detection, object tracking and information extraction; has been extensively investigated. However, little work has been devoted on the problem of discovering global or implicit information from huge video streams. In this paper, a framework has been presented for extracting information for a specified player from soccer video broadcast by data mining techniques. Concepts and information which exist in a soccer video broadcast are useful for team coaches. But, due to various reasons; i.e. wide field of view of a video stream, huge data, existence of great number of important objects in the play field of a soccer match and the occurrence of number of important events, manual extraction of information from soccer video broadcast is difficult and time consuming task. In this paper, a set of techniques is presented that automatically extract some useful information of a player, i.e. velocity and traversed distance, from a soccer video broadcast. Processing of video sequence under change of lighting conditions, fast camera movement and player`s occlusion is a challenging task. Our proposed framework comprise of 3 stages, player segmentation, player tracking and information extraction. All three stages must be robust under various challenges. The performance of our proposed system has been evaluated using a variety of soccer video broadcast having different characteristics in term of lighting conditions. The experiments showed that the efficiency of our system is satisfactory.

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