Human Activity Recognition for Surveillance

Abstract

Abstract: Human activity recognition is important in human-to-human contact and interpersonal relationships. One of the key objects of research in the scientific fields of computer vision and machine learning is the human ability to identify another person's activity. With the introduction of tiny sensor technologies that can be worn on the body, it is now possible to gather and retain data on various aspects of human mobility under free living settings. This technique has the potential to be employed in automated activity profiling systems that generate a continuous record of activity patterns over time. These activity profiling systems rely on classification algorithms to properly interpret body-worn sensor data and identify various activities. This article examines the many strategies used to classify normal activities and/or identify falls using body-worn sensor data. The study is organized according to the many analytical methodologies and highlights the wide range of approaches that have previously been used in this sector. Although tremendous progress has been achieved in this critical field, there is still much room for improvement, particularly in the application of sophisticated classification approaches to situations requiring a wide range of activities. Keywords: Deep Learning, Classification, Detecting Human activity, Image Processing, Feature Extraction, CNN, LR ect.