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A deep unified framework for suspicious action recognition
http://hdl.handle.net/10228/00007499
http://hdl.handle.net/10228/0000749956d7d256-08da-46df-975f-0e76f6dbdc4b
名前 / ファイル | ライセンス | アクション |
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s10015-018-0518-y.pdf (159.8 kB)
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||
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公開日 | 2019-12-19 | |||||||||||
資源タイプ | ||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
資源タイプ | journal article | |||||||||||
タイトル | ||||||||||||
言語 | en | |||||||||||
タイトル | A deep unified framework for suspicious action recognition | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
著者 |
Ilidrissi, Amine
× Ilidrissi, Amine× タン, ジュークイ
WEKO
399
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | As action recognition undergoes change as a field under influence of the recent deep learning trend, and while research in areas such as background subtraction, object segmentation and action classification is steadily progressing, experiments devoted to evaluate a combination of the aforementioned fields, be it from a speed or a performance perspective, are far and few between. In this paper, we propose a deep, unified framework targeted towards suspicious action recognition that takes advantage of recent discoveries, fully leverages the power of convolutional neural networks and strikes a balance between speed and accuracy not accounted for in most research. We carry out performance evaluation on the KTH dataset and attain a 95.4% accuracy in 200 ms computational time, which compares favorably to other state-of-the-art methods. We also apply our framework to a video surveillance dataset and obtain 91.9% accuracy for suspicious actions in 205 ms computational time. | |||||||||||
言語 | en | |||||||||||
備考 | ||||||||||||
内容記述タイプ | Other | |||||||||||
内容記述 | This work was presented in part at the 23rd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 18–20, 2018. | |||||||||||
書誌情報 |
en : Artificial Life and Robotics 巻 24, 号 2, p. 219-224, 発行日 2018-12-19 |
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出版社 | ||||||||||||
言語 | en | |||||||||||
出版者 | Springer | |||||||||||
DOI | ||||||||||||
関連タイプ | isVersionOf | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.1007/s10015-018-0518-y | |||||||||||
日本十進分類法 | ||||||||||||
主題Scheme | NDC | |||||||||||
主題 | 548 | |||||||||||
NCID | ||||||||||||
収録物識別子タイプ | NCID | |||||||||||
収録物識別子 | AA11239104 | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | EISSN | |||||||||||
収録物識別子 | 1614-7456 | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | PISSN | |||||||||||
収録物識別子 | 1433-5298 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | Copyright (c) International Society of Artificial Life and Robotics (ISAROB) 2018 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | The final publication is available at Springer via http://dx.doi.org/https://doi.org/10.1007/s10015-018-0518-y | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Suspicious action recognition | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Deep learning | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Convolutional neural networks | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Background subtraction | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Optical flow estimation | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Action classification | |||||||||||
出版タイプ | ||||||||||||
出版タイプ | AM | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||
査読の有無 | ||||||||||||
値 | yes | |||||||||||
研究者情報 | ||||||||||||
https://hyokadb02.jimu.kyutech.ac.jp/html/35_ja.html | ||||||||||||
論文ID(連携) | ||||||||||||
10333782 | ||||||||||||
連携ID | ||||||||||||
7482 |