人类动作视频关键帧检测的深度学习方法

U. Gawande, K. Hajari, Yogesh Golhar
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引用次数: 11

摘要

关键帧是包含视频集合全部事实的代表性帧。它用于视频的索引、分类、评价和检索。现有算法生成了相关的关键帧,但同时也产生了一些冗余的关键帧。他们中的许多人无法构成整个镜头。在本章中,提出了一种基于深度特征和直方图融合的有效算法来克服这些问题。通过消除关键帧选择的模糊性,提取出最大的相关关键帧。它可以并行和并发地应用于视频序列,从而降低了计算复杂度和时间复杂度。该算法在视频中提取相关关键帧方面的性能表明了它的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Approach to Key Frame Detection in Human Action Videos
A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of consti-tuting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.
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