| Autor: |
Jun-Hwa Kim, Namho Kim, Chee Sun Won |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 9, Pp 123348-123357 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3109904 |
| Popis: |
This paper provides a modular architecture with deep neural networks as a solution for real-time video analytics in an edge-computing environment. The modular architecture consists of two networks of Front-CNN (Convolutional Neural Network) and Back-CNN, where we adopt Shallow 3D CNN (S3D) as the Front-CNN and a pre-trained 2D CNN as the Back-CNN. The S3D (i.e., the Front CNN) is in charge of condensing a sequence of video frames into a feature map with three channels. That is, the S3D takes a set of sequential frames in the video shot as input and yields a learned 3 channel feature map (3CFM) as output. Since the 3CFM is compatible with the three-channel RGB color image format, we can use the output of the S3D (i.e., the 3CFM) as the input to a pre-trained 2D CNN of the Back-CNN for the transfer-learning. This serial connection of Front-CNN and Back-CNN architecture is end-to-end trainable to learn both spatial and temporal information of videos. Experimental results on the public datasets of UCF-Crime and UR-Fall Detection show that the proposed S3D-2DCNN model outperforms the existing methods and achieves state-of-the-art performance. Moreover, since our Front-CNN and Back-CNN modules have a shallow S3D and a light-weighted 2D CNN, respectively, it is suitable for real-time video recognition in edge-computing environments. We have implemented our CNN model on NVIDIA Jetson Nano Developer as an edge-computing device to show its real-time execution. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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