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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Vision Sensor for Automatic Recognition of Human Activities via Hybrid Features and Multi-Class Support Vector

Saleha Kamal¹, Haifa F Alhasson², Mohammed Alnusayri³

¹Department of Computer Science, Air University, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)

|January 11, 2025

Summary

This study presents a new Human Activity Recognition (HAR) system using spatio-temporal features and a Multi-Class Support Vector Machine. The approach achieves high accuracy in identifying human activities across various datasets.

Keywords:

body pose human activity recognition human motion analysis key body points machine learning object detectors spatio-temporal features

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Automated Human Activity Recognition (HAR) is crucial for applications like surveillance and healthcare.
Existing HAR systems face challenges including poor lighting, varied viewing angles, complex clothing, similar gestures, and limited datasets.

Purpose of the Study:

To develop a robust HAR system capable of accurately identifying human activities in diverse settings.
To address the limitations of current HAR systems through effective feature extraction and advanced machine learning models.

Main Methods:

Extraction of unique key body points and full-body features for distinct activity attributes.
Utilizing effective spatio-temporal features for human activity identification.
Employing a Multi-Class Support Vector Machine to enhance classification performance.

Main Results:

The proposed system demonstrates superior performance in classification, accuracy, and generalization on benchmark datasets.
Achieved recognition rates of 88.61% on BIT-Interaction, 87.33% on UT-Interaction, 86.5% on NTU RGB + D 120, and 81.25% on PKUMMD.

Conclusions:

The developed HAR system effectively identifies human activities by leveraging spatio-temporal features and a Multi-Class Support Vector Machine.
The model's high performance across multiple datasets validates its efficiency and robustness in real-world scenarios.