Abstract. We describe an original method for selecting key frames to represent the content of every shot in a video. We aim at spatially sampling in an uniform way the coverage of the scene viewed in each shot. Our method exploits the computation of the dominant image motion (assumed to be due to the camera motion) and mainly relies on geometrical properties related to the incremental contribution of a frame in the considered shot. We also present a refinement of the proposed method to obtain a more accurate representation of the scene, but at the cost of a higher computation time, by considering the iterative minimization of an appropriate energy function. We report experimental results on sports videos and documentaries which demonstrate the accuracy and the efficiency of the proposed approach.
Because of their lack of rules, general broadcast videos are more difficult to analyze than news or sport videos. To retrieve human interventions in this context, a robust face tracker is needed. The approach we investigate for face tracking combines three main modules that are a face detector, a region-based tracker and an eye tracker. The regionbased tracker relies on a robust parametric motion estimation technique. The eye tracker is based on a Kalman filter. The analysis of the coherence of the trackers output provides an efficient way to detect profile positions and tracking errors. We have thus defined an entirely automatic tracker, able to manage several appearing/disappearing faces, without any a priori knowledge on the image sequence. Experimental results on broadcast videos demonstrate its efficiency to deal with large and rapid motions, occlusions and faces in profile position.
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