Paper Title :Investigation on Human Activity Recognition based on Supervised Machine Learning Algorithms
Author :Sam Gilvine Samuvel, Praywin Moses Dass Alex, Akash Ravikumar
Article Citation :Sam Gilvine Samuvel ,Praywin Moses Dass Alex ,Akash Ravikumar ,
(2018 ) " Investigation on Human Activity Recognition based on Supervised Machine Learning Algorithms " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 21-27,
Volume-6, Issue-10
Abstract : Recognizing the activities of humans through computer vision techniques is an important area of research.
This area of research leads to various applications such as patient monitoring, fall detection, surveillance and humancomputer
interface. The capability for recognizing these acts lays foundation for developing highly intelligent and decision
making systems. Generally, most of the mentioned applications requires automatic recognition of high-level activities,
consisting of simple actions of multiple persons. Usually, the intelligence to the system is delivered only if these activities
are properly classified. This paper addresses various machine learning algorithms used in classifying various activities
such as Multi-Layer Perceptron, Random Forest, Naïve Bayes and SVM algorithms. This paper provides classification of
general to complex human activities through comparison study and performance evaluation of these mentioned algorithms
using very large set of images. This review will provide much needed information for further research in more productive
areas.
Keywords: Behavior Analysis and monitoring; Machine learning; Histogram of Oriented Gradients(HOG) Descriptor; Bag
of Visual Words; Local Binary Pattern; SVM; Naïve Bayes; Random Forest; MLP
Type : Research paper
Published : Volume-6, Issue-10
Copyright: © Institute of Research and Journals
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Published on 2018-12-22 |
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