Joint Measurement of Thermal Discomfort by Occupant Pose, Motion and Appearance in Indoor Surveillance Videos for Building Energy Saving

Abstract

To accurately describe the thermal comfort of indoor occupant used to regulate heating, ventilation and air conditioning (HVAC) system is a key goal to reduce energy consumption in intelligent buildings. In this paper, we propose a noncontact measurement of occupant thermal discomfort behavior as an index of thermal comfort. The method takes the existing monitoring image data in the building as the input to infer the occupant thermal discomfort directly, which saves the cost because there is no need to install new sensors in the building and the occupant does not need to wear additional equipment. The framework combined three channels of body posture, motion and performance information to infer occupant thermal discomfort behavior, it consists of a human detection and crop module, a posture analysis module, an optical flow extraction module and a 3D convolutional neural network module. The three channels describe the actions from different perspectives, and the proposed method makes full use of the complementarity of the three modalities to identify the occupant’s thermal discomfort behaviors. Sixteen postures related to thermal discomfort were identified through a questionnaire, and 14,800 video clips containing these postures were collected for experimental evaluation. The results demonstrate the superior performances of our approach to the state-of-the-art techniques. The framework achieves noninvasive, cost-effective thermal comfort evaluation, and has potential value in improving energy efficiency of HVAC systems.