High-Accuracy Automatic Person Segmentation with Novel Spatial Saliency Map
Weijuan Xi, Jianhang Chen, Qian Lin, Jan P. Allebach · 2019
In this work, we propose a high-efficiency person segmentation system that achieves high segmentation accuracy with a much smaller CNN network. In this approach, key-point detection annotation is incorporated for the first time and a novel spatial saliency map, in which the intensity of each pixel indicates the likelihood of forming a part of the human and reflects the distance from the body, is generated to provide more spatial information. Additionally, a lightweight automatic person segmentation network is proposed, which is small and efficient for person segmentation by leveraging atrous convolution. The experimental results prove that an image pyramid resizing augmentation can also improve efficiency. Our proposed segmentation method achieves an accuracy of 94.06% on the person segmentation dataset built in this work, which exceeds the results of previous state-of-the-art methods in accuracy and efficiency.