@article{oai:kyutech.repo.nii.ac.jp:00006289,
 author = {Ilidrissi, Amine and Tan, Joo kooi and タン, ジュークイ},
 issue = {2},
 journal = {Artificial Life and Robotics},
 month = {Dec},
 note = {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., This work was presented in part at the 23rd International Symposium on Artificial Life and Robotics, Beppu, Oita, January 18–20, 2018.},
 pages = {219--224},
 title = {A deep unified framework for suspicious action recognition},
 volume = {24},
 year = {2018}
}