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A development of content-based video summarization system using machine-learning and its application to analysis of livestock behavior
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Paper Abstract

In this work, we propose a static video summarization approach for the analysis of cattle's movement. An original digital video with a length of about 1 hour (58 minutes and 42 seconds) recording the daily behaviors of cattle in cattle barn was applied as the experimental object. In the approach, machine learning, statistics and color histogram are used to extract key-frames from the video. And it can be confirmed that this approach achieve the purpose of video summarization based on cattle's movement through analyzing the results of the experiment.

Paper Details

Date Published: 22 March 2019
PDF: 7 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 1104911 (22 March 2019); doi: 10.1117/12.2522172
Show Author Affiliations
Que Zhi, Tokyo Denki Univ. (Japan)
Tomoko Saitoh, Obihiro Univ. of Agriculture and Veterinary Medicine (Japan)
Mizuki Nakajima, Tokyo Denki Univ. (Japan)
Tsuyoshi Saitoh, Tokyo Denki Univ. (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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