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\section{Introduction}  The current works on soccer match analysis used extra high level data and annotation (such as whoscored, transfertmark) which require human annotation and so preprocessing. In this project we limited our input to the video of a match filmed with a multi-camera system. We focus on an unsupervised classification of the players based on clustering of their trajectories which are extracted automatically from the video of a match during a short period of time. This information is deeply woven into the fabric of team strategy analysis since it is related Bases on this classification we want  to be able to determine  the global placing of the players and the centers leaders  of the cluster may correspond game,  to anticipate  the leader displacement  of the game at that period. Thus we managed players or  to find patterns pattern  in the paths of the players clustering players trajectories. players. This information is deeply woven into the fabric of team strategy analysis and thus lead to high level semantic data about the match.  The problem of tracking players with multiple camera have been achieved consistently for instance in \cite{Ben_Shitrit_2011} even if their paths may intersect over long period of time.   On another hand the clustering process is closely linked to the choice of a metric between the object we want to classify: here the players' trajectories. Most of the time the metrics used are based on euclidian metric. However only discrete trajectories are available in the form of array which can be of different sizes with different time discretization or with different speed and euclidian metric are irrelevant to compare trajectories, mainly because it doesn't take the time parameter into account. So, in our case, a relevant clustering process should be decomposed into 2 parts: