TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
CNN-based Visual/Auditory Feature Fusion Method with Frame Selection for Classifying Video Events |
| 영문제목(English Title) |
CNN-based Visual/Auditory Feature Fusion Method with Frame Selection for Classifying Video Events |
| 저자(Author) |
Giseok Choe
Seungbin Lee
Jongho Nang
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| 원문수록처(Citation) |
VOL 13 NO. 03 PP. 1689 ~ 1701 (2019. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras. A recent report[1] predicted that 80% of the Internet traffic will be video content by the year 2021. Several studies have been conducted on the detection of main video events to manage a large scale of videos. These studies show fairly good performance in certain genres. However, the methods used in previous studies have difficulty in detecting events of personal video. This is because the characteristics and genres of personal videos vary widely. In a research, we found that adding a dataset with the right perspective in the study improved performance. It has also been shown that performance improves depending on how you extract keyframes from the video. we selected frame segments that can represent video considering the characteristics of this personal video. In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module. The proposed method showed mAP 78.4% performance through experiments using LSVC[2] data.
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| 키워드(Keyword) |
Multimedia
Computer Vision Systems
Aritifical Intelligence
Video Classification
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| 파일첨부 |
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