Tracking human motion from video sequences is a well-known video surveillance technique, and many commercially available motion capture devices can now recognize human poses using a depth camera. However, depth camera systems are complicated and have limited optical fields of view. To overcome this problem, it is necessary to develop techniques for recognizing human motion in wide-angle images. In this study, we devised a method for tracking human motion that is robust to wide-angle image distortion. To do so, we developed a new multilayered convolutional neural network architecture for estimating the locations of human body parts in images along with associated transformation parameters that can be applied to a distorted wide-angle image on a frame-by-frame basis. The proposed method was applied to distorted wide-angle images, and its robustness was demonstrated via a quantitative evaluation of human joint prediction and a comparative analysis with a commercially available depth camera-based motion capture system.

中文翻译：

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通过变换参数的迭代搜索从失真的广角图像中进行稳健的人体姿态估计

从视频序列跟踪人体运动是众所周知的视频监控技术，许多商用运动捕捉设备现在可以使用深度相机识别人体姿势。然而，深度相机系统很复杂并且具有有限的光学视野。为了克服这个问题，有必要开发在广角图像中识别人体运动的技术。在这项研究中，我们设计了一种跟踪人体运动的方法，该方法对广角图像失真具有鲁棒性。为此，我们开发了一种新的多层卷积神经网络架构，用于估计图像中人体部位的位置以及相关的变换参数，这些参数可以逐帧应用于失真的广角图像。将所提出的方法应用于扭曲的广角图像，