Automatic Motion-Blurred Hand Matting for Human Soft Segmentation in Videos
Xiaomei Zhao, Yihong Wu · 2019
Accurate hand segmentation is important for human segmentation. However, in videos, hand regions usually have serious motion blur, which reduces segmentation performance obviously. To solve this problem, we propose an automatic matting network to deal with motion-blurred hands. Then we combine the hand alpha mattes provided by matting network and the human segmentation results provided by segmentation network to generate our final human soft segmentation results. In addition, to train the matting network, we need a huge amount of motion-blurred hand images and their groundtruth alpha mattes. However these images are very difficult to obtain. To solve this problem, we propose an efficient semi-automatic synthetic data generation method and generate 36186 synthetic motion-blurred hand images and their alpha mattes. Experiments on synthetic images and real videos show that our method achieves state-of-art matting performance and successfully solve the problem of bad hand segmentation caused by serious motion blur.