This paper considers human motion tracking with multi-view set-ups and investigates a robust strategy that learns online key poses to drive a shape tracking method. The interest arises with realistic dynamic scenes where occlusions or segmentation errors occur. The resulting corrupted observations present missing data and outliers that deteriorate tracking results. In order to cope with such data we propose to use key poses of the tracked person as multiple reference models. In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of the human poses. They can provide therefore better initial hypotheses when tracking with ambiguous and noisy data. Our approach identifies these reference models online, during tracking, as distinctive keyframes. The most suitable one is then chosen as the reference model for the tracking initialization at each frame. In addition, taking advantage of the proximity between successive frames, an efficient outlier handling technique is proposed to prevent the model from associating to irrelevant outliers. The two strategies are successfully experimented with a surface deformation framework that estimates both the pose and the shape. Evaluations and comparisons on existing datasets also demonstrate the benefit of the approach with respect to the state of the art.     «

This paper considers human motion tracking with multi-view set-ups and investigates a robust strategy that learns online key poses to drive a shape tracking method. The interest arises with realistic dynamic scenes where occlusions or segmentation errors occur. The resulting corrupted observations present missing data and outliers that deteriorate tracking results. In order to cope with such data we propose to use key poses of the tracked person as multiple reference models. In contrast to many...     »