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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Vision-based Individual Factors Acquisition for Thermal Comfort Assessment in a Built Environment
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Vision-based Individual Factors Acquisition for Thermal Comfort Assessment in a Built Environment

机译：基于视觉的个性因素在建筑环境中采集热舒适评估

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摘要

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.
机译：为了保持占用者的令人满意的室热环境，供暖，通风和空调（HVAC）系统必须经常工作。然而，房间条件尤其是经验的温度，不能考虑乘客的真实需求，更不用说个性化的热舒适度，因此，HVAC系统未充分利用并且不可避免地诱导能量浪费。为了解决这个问题，提出了一种基于视觉的方法，以获取对评估个性化热敏的多个是关键的多种因素。具体地，对于作为输入的热量摄像机捕获的室内视频，实现了卷积神经网络（CNN），以同时识别乘员的衣服和动作类型。通过20人的数据集，实验结果显示了4个数据集分区的平均分类率为15类场景，这证明了该方法的有效性。

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