@article {170,
	title = {A 3D wrist motion-based sign language video summarization technique},
	journal = {Pattern Recognition Letters},
	volume = {189},
	year = {2025},
	pages = {23-30},
	abstract = {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{\textquoteright}s wrist trajectory to identify keyframes, have been reported in the literature [1]. 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{\textquoteright}s trajectory; in the planar motion case we seek for the maxima of the trajectory{\textquoteright}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) in the gloss classification problem. The results obtained are promising.},
	keywords = {Frenet{\textendash}Serret frame, Sign language, Torsion, video summarization},
	issn = {0167-8655},
	doi = {https://doi.org/10.1016/j.patrec.2024.12.015},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865524003702},
	author = {Evangelos G. Sartinas and Emmanouil Z. Psarakis and Dimitrios I. Kosmopoulos}
}
