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. -IOS Press and the authors. All rights reserved 834 E. Pazouki and M. Rahmati / A novel multimedia data mining framework for information extraction detection and event extraction remain open problems. Thus, investigation on video mining is at its early stage and it normally requires extracting semantic information. The main motivation of video mining is to find and discover knowledge from the stream based on visual and audio cues. The knowledge may typically include structural information within a video clip, association information among various clips, and trend information based on the analysis for a massive size of video set.Recently, mining information in sports video data, especially soccer videos, has become an active research topic. In the data mining technique, some algorithm such as, clustering and classification are performed on the large scale data set, and useful information is extracted. This information may be used for analysis of primary data set in a better way. For example analyzer and coaches are interested to attain useful information of a player from the video data. Extracting these information involves problems such as players detection, players tracking and player occlusion.Furthermore, data mining from human motion has become an important subject in machine vision community. Among many aspects of this area, the sports video analyzing especially, in the ba...
