Seunghyun Kim  Keunhong Chae  Seunghwan Shin  Yusung Kim

10.5626/JOK.2022.49.4.298

ultra wide band Transformer Human Pose Estimation body segmentation

The problem of estimating a human’s pose in specific space from an image is one of the main area of computer vision and is an important technology that can be used in various fields such as games, medical care, disaster, fire fighting, and the military. By combining with machine learning, the accuracy of pose estimation has been greatly improved. However, the image-based approach has a limitation in that it is difficult to estimate pose when part or whole of the body is occluded by obstacles or when the lighting is dark. Recently, studies have emerged to estimate a human pose using wireless signals, which have the advantage of penetrating obstacles without being affected by brightness. The previous stereotype was that two or more pairs of transceivers are required to estimate a specific location based on wireless signals. This paper shows that it is possible to estimate the human pose and to perform body segmentation by applying deep learning only with 1x1 ultra wide band signals collected by 1×1 transceiver. We also propose a method of replacing convolution neural networks and showing better performance through transformer models.