Abstract: 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.

Key words: image processing, contour extraction, body measurement, lightweight neural network; deep learning