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- Title
TGSNET: A FRACTAL NEURAL NETWORK FOR ACTION RECOGNITION.
- Authors
ZHAO, YULAN; LEE, HYO JONG
- Abstract
In the study of action recognition based on optical flow, improving the recognition speed of two-stream neural networks is challenging. In this paper, a new network structure Teacher Guided Student Network (TGSNet) which is based on two-stream and teacher–student architecture is proposed to judge the category of action rapidly in the application. There are two sub-networks with optical flow and RGB frame stream in the network, the optical flow sub-network is assigned as the teacher and the RGB frame stream sub-network as the student. In the training stage, the optical flow sub-network computes the optical flow of the video frame and trains the sub-network then transmits the feature to the RGB frame stream sub-network. The RGB frame stream sub-network uses the RGB frame to mimic the optical flow to train the sub-network. In the test stage, there is only RGB frame stream sub-network existing for action recognition rapidly without computing optical flow. The experimental results show that the TGSNet feeds only by RGB frame stream get a competitive accuracy of 56.7% and a better run-time on HMDB51.
- Subjects
OPTICAL computing; OPTICAL flow; RECOGNITION (Psychology)
- Publication
Fractals, 2023, Vol 31, Issue 6, p1
- ISSN
0218-348X
- Publication type
Article
- DOI
10.1142/S0218348X23401527