Suspicious activity detection using deep learning in secure assisted living IoT environments

Abstract

Children who are left alone in environments such as daycares and creches require constant monitoring and care to protect them from abuse. In this paper, we propose a novel deep learning-based method for predicting the occurrence of abnormal events using footage gathered from networked surveillance systems and notifying users of those events in an Internet of Things (IoT) environment. Sequences of images are converted to still frames and de-blurred using adaptive motion detection techniques. Then, abnormal activities are predicted using random forest differential evolution with kernel density (RFKD), and any abnormal activities that are detected cause signals to be sent to IoT devices via the MQTT protocol. The proposed work consists of a multi-classifier, deep neural network and kernel density functions. The multi-classifier is used for input classifications from the sequence of frames of videos. The deep neural network is used to learn and train the data and kernel density is used clustering and prediction of data. The novelty of the proposed work is in the dynamic nature of activity prediction. Most of the previous work in this research area concentrated on static activity prediction. The proposed work is able to support both static and dynamic activities of daycare environments. In our experimental trials, our novel method’s performance is shown to be superior to that of the ReHAR method.