RGBD 카메라 기반의 Human-Skeleton Keypoints와2-Stacked Bi-LSTM 모델을 이용한 낙상 탐지
Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera
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초록
본 연구에서는 MS Kinect v2 RGBD 카메라 기반의 Human-Skeleton Keypoints와 2-Stacked Bi-LSTM 모델을 이용하여 낙상 행위를 탐지하는방법을 제안한다. 기존의 연구는 RGB 영상에서 OpenPose 등의 딥러닝 모델을 이용하여 골격 정보를 추출한 후 LSTM, GRU 등의 순환신경망모델을 이용해 인식을 수행하였다. 제안한 방법은 카메라로부터 골격정보를 바로 전달 받아 가속도 및 거리의 2개의 시계열 특징을 추출한 후2-Stacked Bi-LSTM 모델을 이용하여 낙상 행위를 인식하였다. 어깨, 척추, 골반 등 주요 골격을 대상으로 중심관절을 구하고 이 중심관절의 움직임가속도와 바닥과의 거리를 특징으로 제안하였다. 추출된 특징은 Stacked LSTM, Bi-LSTM 등의 모델과 성능비교를 수행하였고 GRU, LSTM 등의기존연구에 비해 향상된 검출 성능을 실험을 통해 증명하였다.
Abstract
In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypointsand a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning modelsuch as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposedmethod receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and thenrecognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder,spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extractedfeatures were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existingstudies such as GRU and LSTM was demonstrated through experiments.
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