Human Activity Recognition through ensemble Learning of Convulational neural network

Author(s)	Mohideen Bathusha
Country	India
Abstract	Video classification has been extensively researched in computer vision due to its wide spread use in many important applications such as human action recognition and dynamic scene classification. It is highly desired to have an end-to-end learning framework that can establish effective video representations while simultaneously conducting efficient video classification. Deep learning plays a vital role in image processing. We use Convolutional neural network algorithms for classification. The convolution 3-D (C3-D) and VGG (vision and graphics group) are first deployed to extract temporal and spatial features from the input videos cooperatively, which establishes comprehensive and informative representations of videos.
Keywords	Its main purpose is used to identify human activity
Field	Arts
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-03
DOI	https://doi.org/10.71097/IJSAT.v16.i2.5952
Short DOI	https://doi.org/g9m28p

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International Journal on Science and Technology