An interesting problem in many video-based applications is the generation of short synopses by selecting the most informative frames, a procedure which is known as video summarization. For sign language videos the benefits of using the $t$-parameterized counterpart of the curvature of the 2-D signer's wrist trajectory to identify keyframes, have been recently reported in the literature. In this paper we extend these ideas by modeling the 3-D hand motion that is extracted from each frame of the video. To this end we propose a new informative function based on the $t$-parameterized curvature and torsion of the 3-D trajectory. The method to characterize video frames as keyframes depends on whether the motion occurs in 2-D or 3-D space. Specifically, in the case of 3-D motion we look for the maxima of the harmonic mean of the curvature and torsion of the target's trajectory; in the planar motion case we seek for the maxima of the trajectory's curvature. The proposed 3-D feature is experimentally evaluated in applications of sign language videos on (1) objective measures using ground-truth keyframe annotations, (2) human-based evaluation of understanding, and (3) gloss classification and the results obtained are promising.

翻译：许多视频应用中的一个有趣问题是通过选择信息最丰富的帧来生成简短摘要，这一过程被称为视频摘要。对于手语视频，近期文献报道了利用二维手语者手腕轨迹曲率的$t$参数化对应物来识别关键帧的益处。在本文中，我们通过建模从视频每帧中提取的三维手部动作来扩展这些思想。为此，我们提出了一种基于三维轨迹$t$参数化曲率和挠度的新信息函数。将视频帧表征为关键帧的方法取决于动作发生在二维还是三维空间。具体而言，在三维运动情况下，我们寻找目标轨迹曲率与挠率调和平均值的极大值；在平面运动情况下，我们寻找轨迹曲率的极大值。所提出的三维特征在手语视频应用中通过以下方式进行了实验评估：（1）基于真实关键帧标注的客观度量，（2）基于人类的理解评估，以及（3）词汇分类，所获结果令人振奋。