Semantic segmentation, classifying each pixel in an image to a set of various objects, is an important and necessary problem to understand images. In recent years, convolutional neural networks trained with public datasets enable to segment objects and understand images. However, it is still challenging to segment objects with high accuracy on a simple and small network. In this work, we describe convolutional neural networks with dilated convolutions to segment person accurately especially near boundary using data augmentation technique. Additionally, we develop a smaller network which can run each frame in webcam video faster without degrading segmentation performance. Our method both numerically and visually outperforms other segmentation techniques.