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arXiv · 1711.08922

Summarizing First-Person Videos from Third Persons' Points of Views

Hsuan-I Ho·Wei-Chen Chiu·Yu-Chiang Frank Wang
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Abstract

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small number of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.

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BibTeXRIS

Hsuan-I Ho, Wei-Chen Chiu, Yu-Chiang Frank Wang. 2017-11-24. Summarizing First-Person Videos from Third Persons' Points of Views. https://arxiv.org/abs/1711.08922

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