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Model Architecture Analysis and Extension for Improving RF-based Multi-Person Pose Estimation Performance
http://doi.org/10.5626/JOK.2024.51.3.262
An RF-based multi-person pose estimation system can estimate each human posture even when it is challenging to obtain clear visibility due to obstacles or lighting conditions. Traditionally, a cross-modal teacher-student learning approach has been employed. The approach utilizes pseudo-label data acquired by using images captured concurrently with RF signal collection as input for a pretrained image-based pose estimation model. In a previous research study, the research team applied cross-modal knowledge distillation to mimic the feature maps of image-based learning models and referred to it as "visual cues." This enhanced the performance of RF signal-based pose estimation. In this paper, performance is compared based on the ratio at which the learned visual cues are concatenated, and an analysis of the impact of segmentation mask learning and the use of multiframe inputs on multi-person pose estimation performance is presented. It is demonstrated that the best performance is achieved when visual cues and multiframe inputs are used in combination.
1×1 UWB-based Human Pose Estimation Using Transformer
Seunghyun Kim, Keunhong Chae, Seunghwan Shin, Yusung Kim
http://doi.org/10.5626/JOK.2022.49.4.298
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.
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