Detection of abnormal human behavior using deep learning

Abstract

The complete human body or the various limb postures are involved in human action. These days, Abnormal Human Activity Recognition (Abnormal HAR) is highly well noticed and surveyed in many studies. However, because of complicated difficulties such as sensor movement, positioning, and so on, as well as how individuals carry out their activities, it continues to be a difficult process. Identifying particular activities benefits human-centric applications such as postoperative trauma recovery, gesture detection, exercise, fitness, and home care help. The HAR system has the ability to automate or simplify most of the people’s everyday chores. HAR systems often use supervised or unsupervised learning as their foundation. Unsupervised systems operate according to a set of rules, where as supervised systems need to be trained beforehand using specific datasets. This study conducts detailed literature reviews on the development of various activity identification techniques currently being used. The three methods—wearable device-based, pose-based, and smartphone sensor—are examined in this inquiry for identifying abnormal acts (AAD). The sensors in wearable devices collect data, whereas the gyroscopes and accelerometers in smartphones provide input to the sensors in wearable devices. To categorize activities, pose estimation uses a neural network. The Anomalous Action Detection Dataset (Ano-AAD) is created and improved using several methods. The study examines fresh datasets and innovative models, including UCF-Crime. A new pattern in anomalous HAR systems has emerged, linking anomalous HAR tasks to computer vision applications including security, video surveillance and home monitoring. In terms of issues and potential solutions, the survey looks at vision-based HAR.