Abstrakt

To maintain satisfactory chamber thermal environments for occupants, heating, ventilation and air conditioning (HVAC) systems have to work frequently. However, the
room conditions especially the temperatures are usually set
empirically which fail to consider occupants’ real needs, not
to mention personalized thermal comfort, therefore, the HVAC
systems are underutilized and unavoidably induce energy waste.
To solve this problem, a vision-based method to acquire multiple
individual factors that are critical for assessing personalized
thermal sensation is proposed. Specifically, with the indoor
videos captured by a thermal camera as inputs, a convolutional neural network (CNN) is implemented to recognize
an occupant’s clothes and action type simultaneously. With a
dataset of 20 persons, the experimental results show an average
classification rate of 95.14% on 4 dataset partitions for a 15-
category scenario, which prove the effectiveness of the proposed
method.
OriginalsprogEngelsk
Titel2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
Antal sider5
ForlagIEEE
Publikationsdato2020
Sider372-376
ISBN (Elektronisk)978-1-7281-3079-8
DOI
StatusUdgivet - 2020
Begivenhed2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) - Buenos Aires, Argentina
Varighed: 16 nov. 202020 nov. 2020

Konference

Konference2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
LandArgentina
ByBuenos Aires
Periode16/11/202020/11/2020

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