The present invention relates to a method and system for calculating the occupant activity amount using deep learning-based occupant pose classification, and the method for calculating the occupant activity amount using the deep learning-based occupant pose classification detects the occupant from the indoor image collected by the camera sensor Step of calculating the joint coordinate value of the occupant by learning the characteristics of the occupant image: classifying the indoor activity pose of the occupant through the deep learning by inputting the acquired positional coordinates of the human joint and classifying the amount of activity (MET) Obtaining； And calculating the activity amount of the occupant required for controlling the indoor thermal environment using the indoor activity poses of the occupant classified by a predetermined time unit and the acquired activity amount. According to the present invention, as a model for measuring the MET of the occupants required when introducing the PMV control method for indoor comfort control, the occupant activity amount calculation model can be used to control the indoors along with other environmental variables and improve satisfaction in comfort range. have. Since only the camera sensor is used and the image of the occupant is analyzed to measure the pose and the amount of activity, the occupant does not need to operate or attach the device directly, so it is applicable. In addition, it is possible to reduce errors by determining the actual action being taken, rather than indirectly measuring the incidental information of the occupant's activities.본 발명은 딥러닝 기반의 재실자 포즈 분류를 이용한 재실자 활동량 산출 방법 및 시스템에 관한 것으로서, 그 딥러닝 기반의 재실자 포즈 분류를 이용한 재실자 활동량 산출 방법은 카메라 센서에 의해 수집된 실내 이미지에서 재실자를 검출하고 재실자 이미지의 특징을 학습하여 재실자의 관절 좌표값을 산출하는 단계: 그 획득된 인체관절의 위치 좌표를 입력으로 하여 딥러닝(deep learning)을 통해 재실자의 실내활동 포즈를 분류하고 활동량(MET)을 획득하는 단계； 및 소정시간 단위로 분류되는 재실자의 실내활동 포즈들과 획득되는 활동량을 이용하여 실내 열 환경 제어에 필요한 재실자의 활동량을 산출하는 단계를 포함하여 이루어진다.본 발명에 의하면, 실내의
