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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 2(2) (2020)
Copyright(C) MYU K.K.
pp. 599-610
S&M2125 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2616
Published: February 20, 2020

Automatic Route Video Summarization based on Image Analysis for Intuitive Touristic Experience [PDF]

Yuki Kanaya, Shogo Kawanaka, Hirohiko Suwa, Yutaka Arakawa, and Keiichi Yasumoto

(Received September 11, 2019; Accepted January 20, 2020)

Keywords: automatic route video summarization, image analysis, intuitive touristic experience, decision support, tourist information application

Currently, many tourists search for and watch tourism videos on the Internet when planning a sightseeing tour. In order to quickly plan a sightseeing route, a shorter playback time of tourism videos is desirable. For this purpose, time-lapse playback would be effective. However, the faster the playback is, the lower the degree of comprehension of the viewers will be. In this paper, we propose a novel time-lapse-based video summarization method without the substantial loss of information important for viewers to plan a tour route. In the proposed method, we focus on scene changes in the video. We extract scenes with a certain level of change compared with previous scenes as important (slowly played back) in the summarized video, while other scenes are fast-forwarded. We investigated the appropriate playback speed of sightseeing videos. As a result of a questionnaire, we found that a playback speed between ×4 and ×8 was the most effective for viewers to understand the sightseeing information for tour route planning. In addition, to evaluate the effectiveness of our proposed method, we conducted experiments with 20 participants using a sightseeing video taken in Kyoto. Comparing the video summarized with our method and that summarized manually (by voting for necessary/ unnecessary scenes), our method identified the important scenes with an F-measure of 62.22%.

Corresponding author: Hirohiko Suwa


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yuki Kanaya, Shogo Kawanaka, Hirohiko Suwa, Yutaka Arakawa, and Keiichi Yasumoto, Automatic Route Video Summarization based on Image Analysis for Intuitive Touristic Experience, Sens. Mater., Vol. 32, No. 2, 2020, p. 599-610.



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