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In this paper, we propose a novel global motion estimation technique based on weighted gradient and Displaced Frame Difference (DFD) associated with Wiener estimation. Then, we apply this technique to parse events of a high level of understanding in a cricket game. A user oriented analysis of the game then reveals a distinct connection between the global motion and specific events. By estimating global motion and analysing the temporal evolution of the estimated motion parameters, we present an effective process for the extraction of cricket events, leading to a succes rate of 88.9%.

The content-based indexing task considered in this paper consists in recognizing from their voice, speakers involved in a conversation. A new approach f o r speaker-based segmentation, which is thejrst necessary step f o r this indexing task, is described. Our study is done under the assumptions that no prior information on speakers is available, that the number of speakers is unknown and that people do not speak simultaneously. Audio data indexing is commonly divided in i~o parts : audio data is first segmented with respect to sp

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