复杂场景下的人体轮廓提取及尺寸测量

自然拍摄的人体照片由于背景图案较为复杂，采用传统基于图片色彩空间或能量 梯度的图像处理方法难以准确地识别人体的轮廓。采用神经网络的方法，可以提高识别的精度。 但是，一般的神经网络方法由于计算量与参数规模较大，难以在移动终端部署。因此，提出了 一种轻量级的神经网络策略以提取人体轮廓。该网络采用 MobileNet V2 与 U-Net 框架，通过构 建特定姿态的人体数据集进行训练，识别相应的人体轮廓形状。人体轮廓经过提取关键点、拟 合回归分析等后续处理，可估算人体的尺寸。该方法可应用在移动终端上，通过拍摄的人体照 片的方法测量人体的尺寸。实验表明，该方法能准确地提取复杂背景照片中的人体轮廓并测量 尺寸，在速度与存储占用方面较一般神经网络有一定优势。

Contour recognition and information extraction of human bodies in complex scenes

Due to the complexity of background, it is difficult to accurately recognize the contour of human body by traditional image-processing methods based on color space or energy gradient. Neural network can improve the accuracy of recognition. However, due to the large scale of computations and parameters, it is difficult to deploy the general neural network methods in mobile devices. Therefore, we proposed a lightweight neural network to extract human body contours. This network utilized MobileNet V2 and U-Net framework to recognize the contours of human bodies by building a human-body dataset with specific poses for training. The contours of human bodies can be used to measure the sizes of human bodies after the subsequent processes, such as the extraction of key points and analysis of fitting regression. This method can be applied to mobile terminals to measure the body sizes by taking pictures. Experiments show that this method can accurately extract the contours of human bodies in photos with complex backgrounds and measure the body sizes, and that it possesses some advantages over the general neural network in terms of speed and storage.