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We present a supervised method for the detection and retrieval of relevant events in videos according to dynamic content. We adopt a statistical representation where residual and camera motion informations are characterized by probabilistic models. In an off-line stage, the models associated to pre-identified classes of meaningful dynamic events are learned from a given training set of video samples. Then, a classification and selection algorithm is applied on each segment of a temporal segmentation of the video to process, by exploiting this statistical framework. Only the segments associated to classes defined as relevant in terms of dynamic event can then be selected. The efficiency of the proposed method is evaluated on sport videos for which categories of relevant events can be explicitly defined.