DTEA: optical flow estimation with deep Taylor expansion approximation network

Zifan Zhu; Chen Huang; Zhicheng Wang; Wenduo Xu; Zhenghua Huang

doi:10.1117/12.2680089

27 June 2023 DTEA: optical flow estimation with deep Taylor expansion approximation network

Zifan Zhu, Chen Huang, Zhicheng Wang, Wenduo Xu, Zhenghua Huang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127052T (2023) https://doi.org/10.1117/12.2680089
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Optical flow is one of the most challenging tasks in computer vision for recovering the three-dimensional structure and motion of objects. Traditional hand-crafted priori methods can generate robust results at the cost of highly computational complexity. While deep optical flow estimation methods are impressive for their performance, especially a fast testing speed, but fail in a good interpretability. To cope with these issues, this paper proposes a novel optical flow estimation method, namely deep Taylor expansion approximation (DTEA). We firstly discuss the relationship between optical flow and Taylor unfolding, then introduce the proposed DTEA network in detail, and finally present experimental results. Quantitative as well as qualitative results of experiments on the FlyingChairs dataset validate the proposed DTEA network is effective, of which the performance can be extensively improved when the depth of the unfolding Taylor approximation is increasing.

Citation Download Citation

Zifan Zhu, Chen Huang, Zhicheng Wang, Wenduo Xu, and Zhenghua Huang "DTEA: optical flow estimation with deep Taylor expansion approximation network", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127052T (27 June 2023); https://doi.org/10.1117/12.2680089

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