Research Article

Vision-Based Human Activity Recognition Uses A Deep Learning Approach

by  Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Issue 74
Published: March 2025
Authors: Pranta Kumar Sarkar, Moskura Hoque, Mostofa Kamal Nasir
10.5120/ijca2025924621
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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 (March 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 },
                        year    = { 2025 },
                        volume  = { 186 },
                        number  = { 74 },
                        pages   = { 70-74 },
                        doi     = { 10.5120/ijca2025924621 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2025
                        %A Pranta Kumar Sarkar
                        %A Moskura Hoque
                        %A Mostofa Kamal Nasir
                        %T Vision-Based Human Activity Recognition Uses A Deep Learning Approach%T 
                        %J International Journal of Computer Applications
                        %V 186
                        %N 74
                        %P 70-74
                        %R 10.5120/ijca2025924621
                        %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
No index terms available.
Keywords

Computer Vision Activity Recognition CNN Deep Learning High Performance.

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