A Survey on Human Activity Recognition and Classification

Abstract

Activity Recognition and Classification is one of the most significant issues in the computer vision field. Identifying and recognizing actions or activities that are performed by a person is a primary key goal of intelligent video systems. Human activity is used in a variety of application areas, from human-computer interaction to surveillance, security, and health monitoring systems. Despite ongoing efforts in the field, activity recognition is still a difficult task in an unrestricted environment and faces many challenges. In this paper, we are focusing on some recent research papers on various methods of activity recognition. The work includes three popular methods of recognizing activity, namely vision-based (using pose estimation), wearable devices, and smartphone sensors. We will also discuss some pros and cons of the above technologies and take a view on a brief comparison between their accuracy. The findings will also show how the vision-based approach is becoming a popular approach for HAR research these days.