Vision-based Individual Factors Acquisition for Thermal Comfort Assessment in a Built Environment
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 mentio...
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Published in | 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) pp. 662 - 666 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.11.2020
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Abstract | 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 95.14parcent on 4 dataset partitions for a 15-category scenario, which prove the effectiveness of the proposed method. |
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AbstractList | 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 95.14parcent on 4 dataset partitions for a 15-category scenario, which prove the effectiveness of the proposed method. |
Author | Liu, Jinsong Foged, Isak Worre Moeslund, Thomas B. |
Author_xml | – sequence: 1 givenname: Jinsong surname: Liu fullname: Liu, Jinsong organization: CREATE, Aalborg University,Visual Analysis of People Laboratory,Aalborg,Denmark,9000 – sequence: 2 givenname: Isak Worre surname: Foged fullname: Foged, Isak Worre organization: CREATE, Aalborg University,Section for Architecture and Urban Design,Aalborg,Denmark,9000 – sequence: 3 givenname: Thomas B. surname: Moeslund fullname: Moeslund, Thomas B. organization: CREATE, Aalborg University,Visual Analysis of People Laboratory,Aalborg,Denmark,9000 |
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PublicationTitle | 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) |
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Snippet | To maintain satisfactory chamber thermal environments for occupants, heating, ventilation and air conditioning (HVAC) systems have to work frequently. However,... |
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SubjectTerms | HVAC Optical imaging Skin Temperature measurement Thermometers Training Videos |
Title | Vision-based Individual Factors Acquisition for Thermal Comfort Assessment in a Built Environment |
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