30.07.2020
Suspicious activity detection using deep learning in secure assisted living IoT environments
Erschienen in: The Journal of Supercomputing | Ausgabe 4/2021
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Abstract
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.