Identifying salient poses in lecture videos

The communicative importance of gestures in teaching environments have been widely studied. Two classes of gestures - point and spread gestures - have been identified to indicate pedagogical importance in teaching discourse [1]. In this work, we propose a system for the identification of the poses of point and spread gestures as a preliminary step toward their identification in low-quality unstructured videos. We use a joint-angle descriptor derived from an automatic pose estimation framework to train an SVM in order to classify extracted video frames of an instructor giving a lecture. Ground-truth is collected in the form of 2500 manually annotated frames covering approximately 20 minutes of a video lecture. Cross validation on the ground-truth data showed initial classifier F-scores of 0.54 and 0.39 for point and spread poses.