Variational Optical Flow Computation Method Based on Motion Optimization Semantic Segmentation
GE Liyue1,2, DENG Shixin2, GONG Jie2, ZHANG Congxuan2,3, CHEN Zhen2
1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063; 2. Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 330063; 3. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract：To address the issues of edge-blurring and over-segmentation of image sequence optical flow computation under complex scenes,such as illumination change and large displacement motions, a variational optical flow computation method based on motion optimization semantic segmentation is proposed. Firstly, an energy function of variational optical flow computation is constructed via a image local region based zero-mean normalized cross correlation matching model. Then, the motion boundary information obtained from the computed optical flows is utilized to optimize the initial image semantic segmentation result, and a variational optical flow computation model based on the motion constraint semantic segmentation is constructed. Next, the optical flows of various label areas are fused to acquirethe refined flow field. Finally, experimental results on Middlebury and UCF101 databases demonstrate that the proposed method performs well in computation accuracy and robustness, especially
for the edge preserving with illumination change, textureless regions and large displacement motions.
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2021, Vol. 34 
