Deep Optical Flow Learning Networks Combined with Attention Mechanism

• Main Author: ZHOU Haiyun, XIANG Xuezhi, ZHAI Mingliang, ZHANG Rongfang, WANG Shuai
• Format: Article
• Language: zho
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2020-11-01
• Series: Jisuanji kexue yu tansuo
• Subjects: optical flow estimation; deep learning; attention mechanism; dilated convolution; prior constraints
• Online Access: http://fcst.ceaj.org/CN/abstract/abstract2444.shtml

In order to improve the accuracy of deep learning optical flow estimation based on encoder-decoder U-Net, a modified supervised deep optical flow learning network combined with attention mechanism is proposed, which consists of a contracting part and an expanding part. In contracting part, high-level feature information is ex-tracted using a series of convolutional layers, and spatial feature maps are then restored to full resolution by conducting successive deconvolution in expanding part. In this paper, attention mechanism is embedded in U-Net to learn inter-dependencies among the channels so that the channel-wise features can be weighted adaptively, which can enhance the performance of feature extraction. Meanwhile, the proposed network also combines dilated convolution to enlarge the receptive field without changing the size of convolutional kernel. Further, constancy constraints and smoothness constraints from variational method are also adopted so that priori knowledge can be used to improve the accuracy of optical flow estimation. Extensive experiments are conducted on synthesis image sequence datasets and the experi-mental results show the proposed network is effective for improving accuracy of deep learning optical flow estimation.