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  Published Paper Details:

  Paper Title

Spatiotemporal Fusion Networks For Human Behavior Recognition :Enhancing Channel Attention And Feature Extraction

  Authors

  K. VENU,  K. NAVEEN

  Keywords

Human Behaviour, CNN

  Abstract


This project pioneers a novel method for human behavior recognition, introducing two innovative channel attention modules: the space-time interaction and depth separable convolution modules. Utilizing convolutional neural networks (CNNs), renowned for image and video processing, a multi-scale CNN approach segments behavior videos, applies low-rank learning for behavior information extraction, and integrates findings along the time axis for holistic comprehension. This method not only simplifies information extraction but also adapts flexibly to diverse network structures, enhancing recognition accuracy while minimizing computational complexity. Further, by amalgamating CNN, GRU, and Bidirectional algorithms, the model achieves superior accuracy with just 1000 parameters, outperforming existing algorithms. This hybrid approach optimizes training features, securing even higher accuracy in behavior recognition.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2405114

  Paper ID - 259273

  Page Number(s) - b40-b50

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  K. VENU,  K. NAVEEN,   "Spatiotemporal Fusion Networks For Human Behavior Recognition :Enhancing Channel Attention And Feature Extraction", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.b40-b50, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT2405114.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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