TGSNET: A FRACTAL NEURAL NETWORK FOR ACTION RECOGNITION

Yulan Zhao and Hyo Jong Lee
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Yulan Zhao: Division of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea†Jilin Agricultural Science and Technology University, Jilin 132101, P. R. China
Hyo Jong Lee: Division of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Republic of Korea

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-11

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.

Keywords: Action Recognition; Two-Stream Networks; Teacher–Student Architecture; Optical Flow Sub-Network; RGB Frame Stream Sub-Network (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401527

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