Computer Vision-based Survey on Human Activity Recognition System, Challenges and Applications

Abstract

For surveillance purpose, lots of method were used by the researchers but computer vision based Human Activity Recognition (HAR) technologies/systems received the most interest because they automatically distinguish human behaviour and movements from video data utilizing recorded details from cameras. But the extraction of accurate and opportune information from video of human’s activities and behaviours is most important and difficult task in pervasive computing environment. Due to lots of applications of HAR systems like in medical field, security, visual monitoring, video recovery, entertainment and irregular behaviour detection, the accuracy of system is most important factors for researchers. This review article presents a brief survey of the existing video or vision-based HAR system to find out their challenges and applications in three aspects such as recognition of activities, activity analysis, and decision from visual content representation. In many applications, system recognition time and accuracy is most important factor and it is affected due to an increase in the usage of simple or low quality type cameras for automated systems. So, to obtain a better accuracy and fast responses, the usage of demanding and computationally intelligent classification techniques such as deep learning and machine learning is a better option for researchers. In this survey, we addressed numerous computationally intelligent classification techniques-based research for HAR from 2010 to 2020 for a better analysis of the benefits and drawbacks of systems, the challenges faced and applications with future directions for HAR. We also present some accessible problems and ideas that should be discussed in future research for the HAR system utilizing machine learning and deep learning principles due to their strong relevance.