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Kyushu Institute of Technology, Kitakyushu, Japan
Kyushu Institute of Technology, Kitakyushu, Japan
抄録
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.
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.
雑誌名
Artificial Life and Robotics
巻
24
号
2
ページ
219 - 224
発行年
2018-12-19
出版者
Springer Japan
ISSN
1433-5298
1614-7456
書誌レコードID
AA11239104
DOI
info:doi/10.1007/s10015-018-0518-y
権利
Copyright (c) International Society of Artificial Life and Robotics (ISAROB) 2018
The final publication is available at Springer via http://dx.doi.org/https://doi.org/10.1007/s10015-018-0518-y
A deep unified framework for suspicious action recognition
s10015-018-0518-y.pdf
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