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Utilizing Webcam Interfaces for Enhanced Human Activity Detection

Year: 2025

Type: article

Abstract: :This paper investigates the feasibility and effectiveness of using webcams for real-time human activity recognition (HAR). By leveraging computer vision techniques and deep learning models, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, we explore the classification of human tasks through webcam-based video input. The study utilizes the HMDB-51 dataset to train the models and demonstrate their real-time application. Our results show that the proposed approach successfully identifies several human activities in real time, with an accuracy of over 75%, and demonstrates its potential for applications in surveillance, healthcare monitoring, and interactive gaming.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14958331

Authors: Dr. Yashaswini S, Dr. Jayanthi M G, M Kopinath, Kruthika M, Yukthi S

Language: English

Topic: Human Pose and Action Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949066563

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