来自今天的爱可可AI前沿推介

[CV] DensePose From WiFi

J Geng, D Huang, F D l Torre
[CMU]

基于WiFi的DensePose稠密姿态估计

要点:

  1. 提出一种深度神经网络，将WiFi信号的相位和振幅映射到24个人体区域的UV坐标，以获得稠密的人体姿态对应；
  2. 结果表明，该模型可以仅用WiFi信号作为输入，估计多个目标的稠密姿态，与基于图像的方法相当；
  3. 该方法成本低、广泛可用，且具有隐私保护性，有可能使WiFi设备成为与RGB摄像机和激光雷达相比隐私性更好、不受照明影响、廉价的人体传感器。

一句话总结:
提出了一种深度神经网络，将WiFi信号映射到UV坐标，以进行稠密的人体姿态估计，其性能与基于图像的方法相当，有潜力成为RGB相机和激光雷达的低成本和更有隐私性的替代。

摘要：
计算机视觉和机器学习技术的进步导致了来自RGB相机、激光雷达和雷达的2D和3D人体姿态估计的重大发展。然而，人类对图像的姿态估计受到遮挡和照明的不利影响，这在许多目标场景都很常见。另一方面，雷达和激光雷达技术需要昂贵且耗电的专用硬件。此外，将这些传感器放置在非公共区域会引起严重的隐私问题。为了解决这些局限性，最近的研究探索了使用WiFi天线(1D传感器)进行身体分割和关键点身体检测。本文进一步扩展了WiFi信号与计算机视觉中常用的深度学习架构相结合的使用，以估计稠密的人类姿态对应。本文提出一种深度神经网络，将WiFi信号的相位和振幅映射到24个人体区域的UV坐标。研究结果表明，该模型可通过用WiFi信号作为唯一的输入来估计多个目标人的稠密姿态，其性能与基于图像的方法相当。这为人体传感的低成本、可广泛访问和隐私保护铺平了道路。

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

论文链接：https://arxiv.org/abs/2301.00250
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