Human Activity Recognition Using LSTM Networks

Authors

  • P Sharma CSE, Amity School of Engineering and Technology, Amity University, Gurugram, India
  • S Chaudhary CSE, Amity School of Engineering and Technology, Amity University, Gurugram, India
  • Komal CSE, Amity School of Engineering and Technology, Amity University, Gurugram, India

DOI:

https://doi.org/10.26438/ijcse/v6i3.165167

Keywords:

MLP, LSTM, TDR

Abstract

Deep learning has shown great improvements in all the computer vision and image interpretation tasks. In this paper a fully automated deep model for human activity recognition has been proposed which do not include any prior knowledge. In the first step of the proposed method, model automatically learns all the temporal and spatial features for recognition. In the second stage of the method memory network which is recurrent in nature is used to classify the various human actions. The results obtained from the suggested method are compared with all the rage methods. Outcomes show that the suggested method has better accuracy as compared to various alternative techniques available.

References

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v6i3.165167
Published: 2025-11-12

How to Cite

[1]
P. Sharma, S. Chaudhary, and Komal, “Human Activity Recognition Using LSTM Networks”, Int. J. Comp. Sci. Eng., vol. 6, no. 3, pp. 165–168, Nov. 2025.

Issue

Vol. 6 No. 3 (2018): IJCSE March Edition

Section

Research Article

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