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3D convolutional neural network for home monitoring using low resolution thermal-sensor array

Tao, Lili; Volonakis, Timothy; Tan, Bo; Zhang, Ziqi; Jing, Yanguo; Smith, Melvyn


Tim Volonakis
Research Associate in Computer Vision

Bo Tan

Ziqi Zhang

Yanguo Jing

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Melvyn Smith
Research Centre Director Vision Lab/Prof


The recognition of daily actions, such as walking, sitting or standing, in the home is informative for assisted living, smart homes and general health care. A variety of actions in complex scenes can be recognised using visual information. However cameras succumb to privacy concerns. In this paper, we present a home activity recognition system using an 8×8 infared sensor
array. This low spatial resolution retains user privacy, but is still a powerful representation of actions in a scene. Actions are recognised using a 3D convolutional neural network, extracting not only spatial but temporal information from video sequences. Experimental results obtained from a publicly available dataset Infra-ADL2018 demonstrate a better performance of the proposed approach compared to the state-of-the-art. We show that the sensor is considered better at detecting the occurrence of falls and activities of daily living. Our method achieves an overall accuracy of 97.22% across 7 actions with a
fall detection sensitivity of 100% and specificity of 99.31%.


Tao, L., Volonakis, T., Tan, B., Zhang, Z., Jing, Y., & Smith, M. 3D convolutional neural network for home monitoring using low resolution thermal-sensor array

Presentation Conference Type Conference Paper (unpublished)
Acceptance Date Jan 21, 2019
Peer Reviewed Peer Reviewed
Additional Information Title of Conference or Conference Proceedings : IET International Conference on Technologies for Active and Assisted Living

This file is under embargo due to copyright reasons.

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