Action Recognition Using Frame Average Feature Map with 2D Convolutional Neural Network for Real-Time Video Analysis.
- Authors
- Kang, K.; Park, H.; Shin, J.; Ha, J.; Paik, J.
- Issue Date
- Nov-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
- Journal Title
- 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44020
- DOI
- 10.1109/ICCE-Asia49877.2020.9277163
- ISSN
- 0000-0000
- Abstract
- A typical video action recognition system has a high computational cost and is not suitable for real-time applications. To solve this problem, we propose an action recognition method using a two-dimensional convolutional neural network (2D CNN), which has a significantly lower computational cost than a 3D CNN. In addition, the proposed method uses a small number of frames from the video for an accurate result. The proposed method consists of: i) pretrained VGG16,which is a2D CNN, to train the action of the video andii) a test with an average of ten frames per dataset. The proposed method improved the recognition performance with a reduce computational cost by using the average of several frames instead of directly analyzing all the frames for real-time video analysis environments. © 2020 IEEE.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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