Performance Analysis of Different Classifiers for the Application of Human Activity Identification

Abstract

Recognizing human movements through computer vision is an important field of research, which can be used in various applications such as patient monitoring, observation, and human-machine interface. The ability to perceive these movements requires extremely complex judgments. Generally, the above-mentioned applications need to automatically recognize advanced operations, such as: a pair of easy movements of a man and a woman. If the action is well classified, then the proper information can be provided to the system. This paper addresses various machine learning algorithms such as logistic regression, RBF SVM, decision tree, random forest, linear SVM, gradient boosting DT by grouping different activities. This article classifies complex human behaviors by observing, comparing and evaluating the performance of algorithms by using large set of information.