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Tgsnet: A Fractal Neural Network For Action Recognition

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yulan Zhao & Hyo Jong Lee, 2023. "Tgsnet: A Fractal Neural Network For Action Recognition," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-11.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x23401527
    DOI: 10.1142/S0218348X23401527
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