Identifying salient poses in lecture videos
Identifying salient poses in lecture videos
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识别讲座视频中的显着姿势
DOI：
10.1109/icip.2011.6116113
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
J. Kender
中科院分区：
文献类型：
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作者：
John R. Zhang;J. Kender
手势在教学环境中的交际重要性已经得到了广泛的研究。两类手势-点和扩展手势-已被确定为指示教学话语中的教学重要性[1]。在这项工作中，我们提出了一个系统，用于识别点和传播姿态的姿势，作为在低质量的非结构化视频中识别它们的初步步骤。我们使用一个联合角度描述符来自自动姿态估计框架来训练SVM，以分类提取的视频帧的讲师讲课。地面实况以2500个手动注释帧的形式收集，覆盖大约20分钟的视频讲座。对地面实况数据的交叉验证显示，点和散布姿势的初始分类器F分数为0.54和0.39。
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.