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Vision-based Human Activity Recognition Uses a Deep Learning Approach

by Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 74
Year of Publication: 2025
Authors: Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir
10.5120/ijca2025924621

Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir . Vision-based Human Activity Recognition Uses a Deep Learning Approach. International Journal of Computer Applications. 186, 74 ( Mar 2025), 70-74. DOI=10.5120/ijca2025924621

@article{ 10.5120/ijca2025924621,
author = { Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir },
title = { Vision-based Human Activity Recognition Uses a Deep Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2025 },
volume = { 186 },
number = { 74 },
month = { Mar },
year = { 2025 },
issn = { 0975-8887 },
pages = { 70-74 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number74/vision-based-human-activity-recognition-uses-a-deep-learning-approach/ },
doi = { 10.5120/ijca2025924621 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-25T22:41:41.156168+05:30
%A Pranta Kumar Sarkar
%A Moskura Hoque
%A Mostofa Kamal Nasir
%T Vision-based Human Activity Recognition Uses a Deep Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 74
%P 70-74
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's world, daily life increasingly depends on vision-based advanced technologies, which enhance the reliability and convenience of human lifestyles. Among these technologies, vision-based Human Activity Recognition (HAR) stands out as a comprehensive and challenging field of study, with broad exploration and practical applications. HAR systems are designed to identify diverse human actions under varying environmental conditions.Vision-based activity recognition plays a crucial role in a wide range of applications, including user interface design, robot learning, security surveillance, healthcare, video searching, abnormal activity detection, and human-computer interaction. This study focuses on recognizing various human activities in real-world settings, highlighting the importance of consistency and credibility in the results.To achieve this, data was collected from multiple sources and processed using three distinct models—Convolutional Neural Network (CNN), VGG-16, and ResNet50—to identify the most effective approach for activity recognition. Among these, a specific architectural CNN model was further evaluated for its ability to capture human activity features in specific video sequences. The training, validation, and testing phases utilized a comprehensive dataset comprising 56,690 images. Remarkably, the proposed system achieved an impressive accuracy of 96.23% after 30 epoch running and low validation loss illustrate its effectively recognition each feature.

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Index Terms

Computer Science
Information Sciences

Keywords

Computer Vision Activity Recognition CNN Deep Learning High Performance.

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