@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}
}
@proceedings {18,
	title = {Video summarization guiding evaluative rectification for industrial activity recognition},
	journal = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
	year = {2011},
	month = {nov.},
	pages = {950-957},
	keywords = {activity recognition framework, evaluative rectification method, feature extraction, image recognition, industrial activity recognition, industrial surveillance video, key-frame extraction, neural nets, neural network, production engineering computing, video signal processing, video summarization},
	doi = {10.1109/ICCVW.2011.6130354},
	author = {Voulodimos, A.S. and Doulamis, A.D. and D. Kosmopoulos and Varvarigou, T.A.}
}
