IDEAS home Printed from https://ideas.repec.org/a/hin/complx/6634156.html
   My bibliography  Save this article

Deep ChaosNet for Action Recognition in Videos

Author

Listed:
  • Huafeng Chen
  • Maosheng Zhang
  • Zhengming Gao
  • Yunhong Zhao
  • Zhouchao Wei

Abstract

Current methods of chaos-based action recognition in videos are limited to the artificial feature causing the low recognition accuracy. In this paper, we improve ChaosNet to the deep neural network and apply it to action recognition. First, we extend ChaosNet to deep ChaosNet for extracting action features. Then, we send the features to the low-level LSTM encoder and high-level LSTM encoder for obtaining low-level coding output and high-level coding results, respectively. The agent is a behavior recognizer for producing recognition results. The manager is a hidden layer, responsible for giving behavioral segmentation targets at the high level. Our experiments are executed on two standard action datasets: UCF101 and HMDB51. The experimental results show that the proposed algorithm outperforms the state of the art.

Suggested Citation

  • Huafeng Chen & Maosheng Zhang & Zhengming Gao & Yunhong Zhao & Zhouchao Wei, 2021. "Deep ChaosNet for Action Recognition in Videos," Complexity, Hindawi, vol. 2021, pages 1-5, February.
    • Handle: RePEc:hin:complx:6634156
    DOI: 10.1155/2021/6634156
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2021/6634156.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2021/6634156.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/6634156?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. A.S., Remya Ajai & N.B., Harikrishnan & Nagaraj, Nithin, 2023. "Analysis of logistic map based neurons in neurochaos learning architectures for data classification," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:6634156. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.