Hershey, Pennsylvania
New York, New YorkBeijing, China
Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

Vijayaprabakaran K. (Pondicherry Engineering College, Pondicherry, India), Sathiyamurthy K. (Pondicherry Engineering College, Pondicherry, India), and Ponniamma M. (Pondicherry Engineering College, Pondicherry, India)
Copyright: © 2020 |Pages: 13
DOI: 10.4018/IJSPPC.2020010104
OnDemand:
(Individual Articles)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.
Article Preview
Top

This section discusses the detailed study of state-and-art-of-the solutions for human activity recognition with pros and cons in the perspective of three major approaches as follows.

Complete Article List

Search this Journal:
Reset
Volume 16: 1 Issue (2025): Forthcoming, Available for Pre-Order
Volume 15: 1 Issue (2023)
Volume 14: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 13: 4 Issues (2021)
Volume 12: 4 Issues (2020)
View Complete Journal Contents Listing